Overview

Dataset statistics

Number of variables36
Number of observations2361
Missing cells1581
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory666.3 KiB
Average record size in memory289.0 B

Variable types

Categorical23
Numeric9
Boolean1
Text3

Alerts

Type has constant value ""Constant
mobileInputSelector has constant value ""Constant
continent has constant value ""Constant
fullVisitorId is highly overall correlated with total_visit_counts and 18 other fieldsHigh correlation
onPageTime is highly overall correlated with nextTime and 1 other fieldsHigh correlation
nextTime is highly overall correlated with onPageTime and 1 other fieldsHigh correlation
user_active_days is highly overall correlated with total_unique_session and 19 other fieldsHigh correlation
total_visit_counts is highly overall correlated with fullVisitorId and 18 other fieldsHigh correlation
total_unique_session is highly overall correlated with user_active_days and 17 other fieldsHigh correlation
total_time_spent is highly overall correlated with fullVisitorId and 16 other fieldsHigh correlation
time_spent_per_session is highly overall correlated with onPageTime and 1 other fieldsHigh correlation
AudienceID is highly overall correlated with fullVisitorId and 20 other fieldsHigh correlation
LotameID is highly overall correlated with fullVisitorId and 23 other fieldsHigh correlation
y is highly overall correlated with fullVisitorId and 20 other fieldsHigh correlation
Name is highly overall correlated with fullVisitorId and 20 other fieldsHigh correlation
browser is highly overall correlated with fullVisitorId and 15 other fieldsHigh correlation
operatingSystem is highly overall correlated with fullVisitorId and 18 other fieldsHigh correlation
operatingSystemVersion is highly overall correlated with AudienceID and 13 other fieldsHigh correlation
isMobile is highly overall correlated with fullVisitorId and 18 other fieldsHigh correlation
mobileDeviceBranding is highly overall correlated with user_active_days and 22 other fieldsHigh correlation
mobileDeviceModel is highly overall correlated with fullVisitorId and 23 other fieldsHigh correlation
mobileDeviceInfo is highly overall correlated with fullVisitorId and 22 other fieldsHigh correlation
mobileDeviceMarketingName is highly overall correlated with fullVisitorId and 23 other fieldsHigh correlation
screenResolution is highly overall correlated with fullVisitorId and 23 other fieldsHigh correlation
language is highly overall correlated with user_active_days and 8 other fieldsHigh correlation
subContinent is highly overall correlated with fullVisitorId and 13 other fieldsHigh correlation
country is highly overall correlated with fullVisitorId and 17 other fieldsHigh correlation
region is highly overall correlated with fullVisitorId and 21 other fieldsHigh correlation
city is highly overall correlated with fullVisitorId and 22 other fieldsHigh correlation
GAClientId is highly overall correlated with fullVisitorId and 23 other fieldsHigh correlation
cookie_id is highly overall correlated with fullVisitorId and 23 other fieldsHigh correlation
LotameID is highly imbalanced (57.4%)Imbalance
browser is highly imbalanced (70.6%)Imbalance
operatingSystem is highly imbalanced (68.2%)Imbalance
operatingSystemVersion is highly imbalanced (74.4%)Imbalance
isMobile is highly imbalanced (68.2%)Imbalance
mobileDeviceBranding is highly imbalanced (74.6%)Imbalance
mobileDeviceModel is highly imbalanced (61.1%)Imbalance
mobileDeviceInfo is highly imbalanced (57.4%)Imbalance
mobileDeviceMarketingName is highly imbalanced (60.6%)Imbalance
screenResolution is highly imbalanced (51.6%)Imbalance
language is highly imbalanced (56.8%)Imbalance
subContinent is highly imbalanced (91.0%)Imbalance
country is highly imbalanced (73.5%)Imbalance
region is highly imbalanced (50.4%)Imbalance
city is highly imbalanced (52.8%)Imbalance
GAClientId is highly imbalanced (57.4%)Imbalance
mobileDeviceBranding has 136 (5.8%) missing valuesMissing
mobileDeviceModel has 136 (5.8%) missing valuesMissing
mobileInputSelector has 136 (5.8%) missing valuesMissing
mobileDeviceMarketingName has 136 (5.8%) missing valuesMissing
sub_sec has 1036 (43.9%) missing valuesMissing
onPageTime has 74 (3.1%) zerosZeros

Reproduction

Analysis started2023-06-07 13:51:34.415253
Analysis finished2023-06-07 13:51:52.090751
Duration17.68 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

AudienceID
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
809024
1436 
818744
623 
809023
302 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters14166
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row818744
2nd row818744
3rd row818744
4th row809023
5th row809023

Common Values

ValueCountFrequency (%)
809024 1436
60.8%
818744 623
26.4%
809023 302
 
12.8%

Length

2023-06-07T21:51:52.179796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T21:51:52.293166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
809024 1436
60.8%
818744 623
26.4%
809023 302
 
12.8%

Most occurring characters

ValueCountFrequency (%)
0 3476
24.5%
8 2984
21.1%
4 2682
18.9%
9 1738
12.3%
2 1738
12.3%
1 623
 
4.4%
7 623
 
4.4%
3 302
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14166
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3476
24.5%
8 2984
21.1%
4 2682
18.9%
9 1738
12.3%
2 1738
12.3%
1 623
 
4.4%
7 623
 
4.4%
3 302
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14166
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3476
24.5%
8 2984
21.1%
4 2682
18.9%
9 1738
12.3%
2 1738
12.3%
1 623
 
4.4%
7 623
 
4.4%
3 302
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3476
24.5%
8 2984
21.1%
4 2682
18.9%
9 1738
12.3%
2 1738
12.3%
1 623
 
4.4%
7 623
 
4.4%
3 302
 
2.1%

LotameID
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct42
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
196c30f4cdae19dd70c4d634a38b6b37
1320 
846349b894fb8ab473d5940ec70cae0
519 
bf8e8d0a3026f468d80969ab351af4ce
144 
81f5fed7165c2ab7995465dbf6d5ee61
 
68
37d0d3bf3d3c48261908dddd9cbf02cc
 
39
Other values (37)
271 

Length

Max length32
Median length32
Mean length31.777213
Min length31

Characters and Unicode

Total characters75026
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.5%

Sample

1st rowcbb561a1af5a40eb684d55a8c0b2047a
2nd rowcbb561a1af5a40eb684d55a8c0b2047a
3rd rowcbb561a1af5a40eb684d55a8c0b2047a
4th rowe74bb87a1abb30abf105f2900a65e842
5th rowe74bb87a1abb30abf105f2900a65e842

Common Values

ValueCountFrequency (%)
196c30f4cdae19dd70c4d634a38b6b37 1320
55.9%
846349b894fb8ab473d5940ec70cae0 519
 
22.0%
bf8e8d0a3026f468d80969ab351af4ce 144
 
6.1%
81f5fed7165c2ab7995465dbf6d5ee61 68
 
2.9%
37d0d3bf3d3c48261908dddd9cbf02cc 39
 
1.7%
9d0a70aa93abb38ecf4bd5c5c958e481 30
 
1.3%
444b57aeb4a368852b342a19bfe668a3 30
 
1.3%
9460c27b3ef92bc516bd63956d48f00f 28
 
1.2%
5c0da43ae39ee33d96147196c726d45c 25
 
1.1%
808a7d14ad2bc2eb19aeaa0b7ee9133d 19
 
0.8%
Other values (32) 139
 
5.9%

Length

2023-06-07T21:51:52.390039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
196c30f4cdae19dd70c4d634a38b6b37 1320
55.9%
846349b894fb8ab473d5940ec70cae0 519
 
22.0%
bf8e8d0a3026f468d80969ab351af4ce 144
 
6.1%
81f5fed7165c2ab7995465dbf6d5ee61 68
 
2.9%
37d0d3bf3d3c48261908dddd9cbf02cc 39
 
1.7%
9d0a70aa93abb38ecf4bd5c5c958e481 30
 
1.3%
444b57aeb4a368852b342a19bfe668a3 30
 
1.3%
9460c27b3ef92bc516bd63956d48f00f 28
 
1.2%
5c0da43ae39ee33d96147196c726d45c 25
 
1.1%
f301629668799c18317f0efd9ae7da61 19
 
0.8%
Other values (32) 139
 
5.9%

Most occurring characters

ValueCountFrequency (%)
4 7540
10.0%
3 7436
9.9%
d 7076
9.4%
c 5880
 
7.8%
6 5793
 
7.7%
b 5376
 
7.2%
9 5294
 
7.1%
0 5231
 
7.0%
a 4830
 
6.4%
7 4294
 
5.7%
Other values (6) 16276
21.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45391
60.5%
Lowercase Letter 29635
39.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 7540
16.6%
3 7436
16.4%
6 5793
12.8%
9 5294
11.7%
0 5231
11.5%
7 4294
9.5%
8 4050
8.9%
1 3486
7.7%
5 1531
 
3.4%
2 736
 
1.6%
Lowercase Letter
ValueCountFrequency (%)
d 7076
23.9%
c 5880
19.8%
b 5376
18.1%
a 4830
16.3%
e 3463
11.7%
f 3010
10.2%

Most occurring scripts

ValueCountFrequency (%)
Common 45391
60.5%
Latin 29635
39.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 7540
16.6%
3 7436
16.4%
6 5793
12.8%
9 5294
11.7%
0 5231
11.5%
7 4294
9.5%
8 4050
8.9%
1 3486
7.7%
5 1531
 
3.4%
2 736
 
1.6%
Latin
ValueCountFrequency (%)
d 7076
23.9%
c 5880
19.8%
b 5376
18.1%
a 4830
16.3%
e 3463
11.7%
f 3010
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 7540
10.0%
3 7436
9.9%
d 7076
9.4%
c 5880
 
7.8%
6 5793
 
7.7%
b 5376
 
7.2%
9 5294
 
7.1%
0 5231
 
7.0%
a 4830
 
6.4%
7 4294
 
5.7%
Other values (6) 16276
21.7%

y
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
10000
1436 
0
623 
5000
302 

Length

Max length5
Median length5
Mean length3.8166031
Min length1

Characters and Unicode

Total characters9011
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row5000
5th row5000

Common Values

ValueCountFrequency (%)
10000 1436
60.8%
0 623
26.4%
5000 302
 
12.8%

Length

2023-06-07T21:51:52.535975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T21:51:52.655807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
10000 1436
60.8%
0 623
26.4%
5000 302
 
12.8%

Most occurring characters

ValueCountFrequency (%)
0 7273
80.7%
1 1436
 
15.9%
5 302
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9011
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7273
80.7%
1 1436
 
15.9%
5 302
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9011
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7273
80.7%
1 1436
 
15.9%
5 302
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9011
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7273
80.7%
1 1436
 
15.9%
5 302
 
3.4%

Name
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
RMG - 1PD - Declared - HHI - RM5k - 10k+
1436 
RMG - 1PD - Declared - HHI - <RM1,500
623 
RMG - 1PD - Declared - HHI - RM1.5K - RM5K
302 

Length

Max length43
Median length41
Mean length40.46421
Min length38

Characters and Unicode

Total characters95536
Distinct characters25
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRMG - 1PD - Declared - HHI - <RM1,500
2nd rowRMG - 1PD - Declared - HHI - <RM1,500
3rd rowRMG - 1PD - Declared - HHI - <RM1,500
4th rowRMG - 1PD - Declared - HHI - RM1.5K - RM5K
5th rowRMG - 1PD - Declared - HHI - RM1.5K - RM5K

Common Values

ValueCountFrequency (%)
RMG - 1PD - Declared - HHI - RM5k - 10k+ 1436
60.8%
RMG - 1PD - Declared - HHI - <RM1,500 623
26.4%
RMG - 1PD - Declared - HHI - RM1.5K - RM5K 302
 
12.8%

Length

2023-06-07T21:51:52.757388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T21:51:52.903142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
11182
45.2%
rmg 2361
 
9.5%
1pd 2361
 
9.5%
declared 2361
 
9.5%
hhi 2361
 
9.5%
rm5k 1738
 
7.0%
10k 1436
 
5.8%
rm1,500 623
 
2.5%
rm1.5k 302
 
1.2%

Most occurring characters

ValueCountFrequency (%)
22364
23.4%
- 11182
11.7%
R 5024
 
5.3%
M 5024
 
5.3%
H 4722
 
4.9%
1 4722
 
4.9%
D 4722
 
4.9%
e 4722
 
4.9%
k 2872
 
3.0%
0 2682
 
2.8%
Other values (15) 27500
28.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 27179
28.4%
Space Separator 22364
23.4%
Lowercase Letter 19399
20.3%
Dash Punctuation 11182
11.7%
Decimal Number 10067
 
10.5%
Control 2361
 
2.5%
Math Symbol 2059
 
2.2%
Other Punctuation 925
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 5024
18.5%
M 5024
18.5%
H 4722
17.4%
D 4722
17.4%
I 2361
8.7%
P 2361
8.7%
G 2361
8.7%
K 604
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
e 4722
24.3%
k 2872
14.8%
r 2361
12.2%
d 2361
12.2%
a 2361
12.2%
l 2361
12.2%
c 2361
12.2%
Decimal Number
ValueCountFrequency (%)
1 4722
46.9%
0 2682
26.6%
5 2663
26.5%
Math Symbol
ValueCountFrequency (%)
+ 1436
69.7%
< 623
30.3%
Other Punctuation
ValueCountFrequency (%)
, 623
67.4%
. 302
32.6%
Space Separator
ValueCountFrequency (%)
22364
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11182
100.0%
Control
ValueCountFrequency (%)
2361
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48958
51.2%
Latin 46578
48.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 5024
10.8%
M 5024
10.8%
H 4722
10.1%
D 4722
10.1%
e 4722
10.1%
k 2872
 
6.2%
I 2361
 
5.1%
r 2361
 
5.1%
d 2361
 
5.1%
a 2361
 
5.1%
Other values (5) 10048
21.6%
Common
ValueCountFrequency (%)
22364
45.7%
- 11182
22.8%
1 4722
 
9.6%
0 2682
 
5.5%
5 2663
 
5.4%
2361
 
4.8%
+ 1436
 
2.9%
< 623
 
1.3%
, 623
 
1.3%
. 302
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22364
23.4%
- 11182
11.7%
R 5024
 
5.3%
M 5024
 
5.3%
H 4722
 
4.9%
1 4722
 
4.9%
D 4722
 
4.9%
e 4722
 
4.9%
k 2872
 
3.0%
0 2682
 
2.8%
Other values (15) 27500
28.8%

Type
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
HHI
2361 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7083
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHHI
2nd rowHHI
3rd rowHHI
4th rowHHI
5th rowHHI

Common Values

ValueCountFrequency (%)
HHI 2361
100.0%

Length

2023-06-07T21:51:53.008485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T21:51:53.325240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
hhi 2361
100.0%

Most occurring characters

ValueCountFrequency (%)
H 4722
66.7%
I 2361
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7083
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H 4722
66.7%
I 2361
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 7083
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
H 4722
66.7%
I 2361
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7083
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H 4722
66.7%
I 2361
33.3%

fullVisitorId
Real number (ℝ)

Distinct42
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5315081 × 1018
Minimum9.7055929 × 1016
Maximum1.6948168 × 1019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.9 KiB
2023-06-07T21:51:53.417481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9.7055929 × 1016
5-th percentile5.1309189 × 1017
Q15.1979895 × 1018
median8.5478763 × 1018
Q38.5478763 × 1018
95-th percentile8.5478763 × 1018
Maximum1.6948168 × 1019
Range1.6851112 × 1019
Interquartile range (IQR)3.3498868 × 1018

Descriptive statistics

Standard deviation2.6939493 × 1018
Coefficient of variation (CV)0.41245441
Kurtosis-0.15095488
Mean6.5315081 × 1018
Median Absolute Deviation (MAD)0
Skewness-0.98803325
Sum1.5420891 × 1022
Variance7.257363 × 1036
MonotonicityNot monotonic
2023-06-07T21:51:53.562083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
8.547876345 × 10181320
55.9%
5.197989545 × 1018519
 
22.0%
5.130918873 × 1017144
 
6.1%
1.483772561 × 101868
 
2.9%
6.287112969 × 101839
 
1.7%
3.414056058 × 101830
 
1.3%
1.825853035 × 101830
 
1.3%
3.451615186 × 101828
 
1.2%
6.927050101 × 101725
 
1.1%
9.121685819 × 101819
 
0.8%
Other values (32) 139
 
5.9%
ValueCountFrequency (%)
9.705592879 × 10167
 
0.3%
3.140949715 × 10171
 
< 0.1%
5.130918873 × 1017144
6.1%
6.927050101 × 101725
 
1.1%
8.137922027 × 10174
 
0.2%
1.158781766 × 10183
 
0.1%
1.316008587 × 10182
 
0.1%
1.345556032 × 10185
 
0.2%
1.483772561 × 101868
2.9%
1.654204474 × 10181
 
< 0.1%
ValueCountFrequency (%)
1.694816805 × 10191
 
< 0.1%
1.053134259 × 10193
 
0.1%
9.121685819 × 101819
 
0.8%
8.70898808 × 10181
 
< 0.1%
8.547876345 × 10181320
55.9%
8.017158308 × 10182
 
0.1%
7.791512354 × 10184
 
0.2%
7.690537217 × 10182
 
0.1%
7.509468616 × 10187
 
0.3%
7.213995735 × 10181
 
< 0.1%

browser
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
Chrome
2164 
Android Webview
 
167
Samsung Internet
 
30

Length

Max length16
Median length6
Mean length6.7636595
Min length6

Characters and Unicode

Total characters15969
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChrome
2nd rowChrome
3rd rowChrome
4th rowChrome
5th rowChrome

Common Values

ValueCountFrequency (%)
Chrome 2164
91.7%
Android Webview 167
 
7.1%
Samsung Internet 30
 
1.3%

Length

2023-06-07T21:51:53.688267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T21:51:53.785887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
chrome 2164
84.6%
android 167
 
6.5%
webview 167
 
6.5%
samsung 30
 
1.2%
internet 30
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 2558
16.0%
r 2361
14.8%
o 2331
14.6%
m 2194
13.7%
C 2164
13.6%
h 2164
13.6%
d 334
 
2.1%
i 334
 
2.1%
n 257
 
1.6%
197
 
1.2%
Other values (12) 1075
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13214
82.7%
Uppercase Letter 2558
 
16.0%
Space Separator 197
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2558
19.4%
r 2361
17.9%
o 2331
17.6%
m 2194
16.6%
h 2164
16.4%
d 334
 
2.5%
i 334
 
2.5%
n 257
 
1.9%
v 167
 
1.3%
w 167
 
1.3%
Other values (6) 347
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
C 2164
84.6%
W 167
 
6.5%
A 167
 
6.5%
S 30
 
1.2%
I 30
 
1.2%
Space Separator
ValueCountFrequency (%)
197
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15772
98.8%
Common 197
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2558
16.2%
r 2361
15.0%
o 2331
14.8%
m 2194
13.9%
C 2164
13.7%
h 2164
13.7%
d 334
 
2.1%
i 334
 
2.1%
n 257
 
1.6%
v 167
 
1.1%
Other values (11) 908
 
5.8%
Common
ValueCountFrequency (%)
197
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15969
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2558
16.0%
r 2361
14.8%
o 2331
14.6%
m 2194
13.7%
C 2164
13.6%
h 2164
13.6%
d 334
 
2.1%
i 334
 
2.1%
n 257
 
1.6%
197
 
1.2%
Other values (12) 1075
6.7%

operatingSystem
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
Android
2225 
Windows
 
136

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters16527
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAndroid
2nd rowAndroid
3rd rowAndroid
4th rowWindows
5th rowWindows

Common Values

ValueCountFrequency (%)
Android 2225
94.2%
Windows 136
 
5.8%

Length

2023-06-07T21:51:53.877290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T21:51:53.976907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
android 2225
94.2%
windows 136
 
5.8%

Most occurring characters

ValueCountFrequency (%)
d 4586
27.7%
n 2361
14.3%
o 2361
14.3%
i 2361
14.3%
A 2225
13.5%
r 2225
13.5%
W 136
 
0.8%
w 136
 
0.8%
s 136
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14166
85.7%
Uppercase Letter 2361
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 4586
32.4%
n 2361
16.7%
o 2361
16.7%
i 2361
16.7%
r 2225
15.7%
w 136
 
1.0%
s 136
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
A 2225
94.2%
W 136
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 16527
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 4586
27.7%
n 2361
14.3%
o 2361
14.3%
i 2361
14.3%
A 2225
13.5%
r 2225
13.5%
W 136
 
0.8%
w 136
 
0.8%
s 136
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16527
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 4586
27.7%
n 2361
14.3%
o 2361
14.3%
i 2361
14.3%
A 2225
13.5%
r 2225
13.5%
W 136
 
0.8%
w 136
 
0.8%
s 136
 
0.8%

operatingSystemVersion
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
11
1895 
10
402 
9
 
38
8.0.0
 
8
8.1.0
 
6
Other values (6)
 
12

Length

Max length5
Median length2
Mean length2.0072003
Min length1

Characters and Unicode

Total characters4739
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st row8.1.0
2nd row8.1.0
3rd row8.1.0
4th row7
5th row7

Common Values

ValueCountFrequency (%)
11 1895
80.3%
10 402
 
17.0%
9 38
 
1.6%
8.0.0 8
 
0.3%
8.1.0 6
 
0.3%
8.1 4
 
0.2%
7 3
 
0.1%
7.1.1 2
 
0.1%
12 1
 
< 0.1%
6.0.1 1
 
< 0.1%

Length

2023-06-07T21:51:54.074104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11 1895
80.3%
10 402
 
17.0%
9 38
 
1.6%
8.0.0 8
 
0.3%
8.1.0 6
 
0.3%
8.1 4
 
0.2%
7 3
 
0.1%
7.1.1 2
 
0.1%
12 1
 
< 0.1%
6.0.1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 4209
88.8%
0 425
 
9.0%
. 40
 
0.8%
9 38
 
0.8%
8 18
 
0.4%
7 6
 
0.1%
2 2
 
< 0.1%
6 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4699
99.2%
Other Punctuation 40
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4209
89.6%
0 425
 
9.0%
9 38
 
0.8%
8 18
 
0.4%
7 6
 
0.1%
2 2
 
< 0.1%
6 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4739
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4209
88.8%
0 425
 
9.0%
. 40
 
0.8%
9 38
 
0.8%
8 18
 
0.4%
7 6
 
0.1%
2 2
 
< 0.1%
6 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4739
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4209
88.8%
0 425
 
9.0%
. 40
 
0.8%
9 38
 
0.8%
8 18
 
0.4%
7 6
 
0.1%
2 2
 
< 0.1%
6 1
 
< 0.1%

isMobile
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.8 KiB
True
2225 
False
 
136
ValueCountFrequency (%)
True 2225
94.2%
False 136
 
5.8%
2023-06-07T21:51:54.176255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

mobileDeviceBranding
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct10
Distinct (%)0.4%
Missing136
Missing (%)5.8%
Memory size36.9 KiB
Samsung
1911 
Huawei
200 
Xiaomi
 
42
Vivo
 
23
Razer
 
19
Other values (5)
 
30

Length

Max length7
Median length7
Mean length6.814382
Min length4

Characters and Unicode

Total characters15162
Distinct characters25
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowXiaomi
2nd rowXiaomi
3rd rowXiaomi
4th rowSamsung
5th rowSamsung

Common Values

ValueCountFrequency (%)
Samsung 1911
80.9%
Huawei 200
 
8.5%
Xiaomi 42
 
1.8%
Vivo 23
 
1.0%
Razer 19
 
0.8%
Nokia 16
 
0.7%
Asus 5
 
0.2%
OPPO 4
 
0.2%
Realme 4
 
0.2%
Neffos 1
 
< 0.1%
(Missing) 136
 
5.8%

Length

2023-06-07T21:51:54.257366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T21:51:54.434422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
samsung 1911
85.9%
huawei 200
 
9.0%
xiaomi 42
 
1.9%
vivo 23
 
1.0%
razer 19
 
0.9%
nokia 16
 
0.7%
asus 5
 
0.2%
oppo 4
 
0.2%
realme 4
 
0.2%
neffos 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 2192
14.5%
u 2116
14.0%
m 1957
12.9%
s 1922
12.7%
S 1911
12.6%
n 1911
12.6%
g 1911
12.6%
i 323
 
2.1%
e 228
 
1.5%
H 200
 
1.3%
Other values (15) 491
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12925
85.2%
Uppercase Letter 2237
 
14.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2192
17.0%
u 2116
16.4%
m 1957
15.1%
s 1922
14.9%
n 1911
14.8%
g 1911
14.8%
i 323
 
2.5%
e 228
 
1.8%
w 200
 
1.5%
o 82
 
0.6%
Other values (6) 83
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
S 1911
85.4%
H 200
 
8.9%
X 42
 
1.9%
V 23
 
1.0%
R 23
 
1.0%
N 17
 
0.8%
O 8
 
0.4%
P 8
 
0.4%
A 5
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 15162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2192
14.5%
u 2116
14.0%
m 1957
12.9%
s 1922
12.7%
S 1911
12.6%
n 1911
12.6%
g 1911
12.6%
i 323
 
2.1%
e 228
 
1.5%
H 200
 
1.3%
Other values (15) 491
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2192
14.5%
u 2116
14.0%
m 1957
12.9%
s 1922
12.7%
S 1911
12.6%
n 1911
12.6%
g 1911
12.6%
i 323
 
2.1%
e 228
 
1.5%
H 200
 
1.3%
Other values (15) 491
 
3.2%

mobileDeviceModel
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct33
Distinct (%)1.5%
Missing136
Missing (%)5.8%
Memory size36.9 KiB
SM-N975F
1320 
SM-A505F
520 
YAL-L21
150 
Redmi Note 9S
 
39
SM-G960F
 
30
Other values (28)
166 

Length

Max length13
Median length8
Mean length7.9006742
Min length2

Characters and Unicode

Total characters17579
Distinct characters41
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.4%

Sample

1st rowRedmi 6A
2nd rowRedmi 6A
3rd rowRedmi 6A
4th rowSM-J730G
5th rowSM-J730G

Common Values

ValueCountFrequency (%)
SM-N975F 1320
55.9%
SM-A505F 520
 
22.0%
YAL-L21 150
 
6.4%
Redmi Note 9S 39
 
1.7%
SM-G960F 30
 
1.3%
MAR-LX2 30
 
1.3%
1819 19
 
0.8%
Phone 2 19
 
0.8%
SM-N770F 18
 
0.8%
3.2 16
 
0.7%
Other values (23) 64
 
2.7%
(Missing) 136
 
5.8%

Length

2023-06-07T21:51:54.562278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sm-n975f 1320
56.7%
sm-a505f 520
 
22.4%
yal-l21 150
 
6.4%
redmi 42
 
1.8%
note 39
 
1.7%
9s 39
 
1.7%
sm-g960f 30
 
1.3%
mar-lx2 30
 
1.3%
2 19
 
0.8%
1819 19
 
0.8%
Other values (27) 118
 
5.1%

Most occurring characters

ValueCountFrequency (%)
5 2366
13.5%
- 2111
12.0%
M 1955
11.1%
S 1950
11.1%
F 1903
10.8%
9 1436
8.2%
N 1397
7.9%
7 1359
7.7%
A 705
 
4.0%
0 611
 
3.5%
Other values (31) 1786
10.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8665
49.3%
Decimal Number 6325
36.0%
Dash Punctuation 2111
 
12.0%
Lowercase Letter 361
 
2.1%
Space Separator 101
 
0.6%
Other Punctuation 16
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 1955
22.6%
S 1950
22.5%
F 1903
22.0%
N 1397
16.1%
A 705
 
8.1%
L 365
 
4.2%
Y 152
 
1.8%
R 77
 
0.9%
X 47
 
0.5%
G 42
 
0.5%
Other values (10) 72
 
0.8%
Decimal Number
ValueCountFrequency (%)
5 2366
37.4%
9 1436
22.7%
7 1359
21.5%
0 611
 
9.7%
2 237
 
3.7%
1 212
 
3.4%
6 51
 
0.8%
8 27
 
0.4%
3 21
 
0.3%
4 5
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 100
27.7%
o 58
16.1%
i 42
11.6%
m 42
11.6%
d 42
11.6%
t 39
 
10.8%
n 19
 
5.3%
h 19
 
5.3%
Dash Punctuation
ValueCountFrequency (%)
- 2111
100.0%
Space Separator
ValueCountFrequency (%)
101
100.0%
Other Punctuation
ValueCountFrequency (%)
. 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9026
51.3%
Common 8553
48.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 1955
21.7%
S 1950
21.6%
F 1903
21.1%
N 1397
15.5%
A 705
 
7.8%
L 365
 
4.0%
Y 152
 
1.7%
e 100
 
1.1%
R 77
 
0.9%
o 58
 
0.6%
Other values (18) 364
 
4.0%
Common
ValueCountFrequency (%)
5 2366
27.7%
- 2111
24.7%
9 1436
16.8%
7 1359
15.9%
0 611
 
7.1%
2 237
 
2.8%
1 212
 
2.5%
101
 
1.2%
6 51
 
0.6%
8 27
 
0.3%
Other values (3) 42
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17579
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2366
13.5%
- 2111
12.0%
M 1955
11.1%
S 1950
11.1%
F 1903
10.8%
9 1436
8.2%
N 1397
7.9%
7 1359
7.7%
A 705
 
4.0%
0 611
 
3.5%
Other values (31) 1786
10.2%

mobileInputSelector
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing136
Missing (%)5.8%
Memory size36.9 KiB
touchscreen
2225 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters24475
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtouchscreen
2nd rowtouchscreen
3rd rowtouchscreen
4th rowtouchscreen
5th rowtouchscreen

Common Values

ValueCountFrequency (%)
touchscreen 2225
94.2%
(Missing) 136
 
5.8%

Length

2023-06-07T21:51:54.655903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T21:51:54.756858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
touchscreen 2225
100.0%

Most occurring characters

ValueCountFrequency (%)
c 4450
18.2%
e 4450
18.2%
t 2225
9.1%
o 2225
9.1%
u 2225
9.1%
h 2225
9.1%
s 2225
9.1%
r 2225
9.1%
n 2225
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24475
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 4450
18.2%
e 4450
18.2%
t 2225
9.1%
o 2225
9.1%
u 2225
9.1%
h 2225
9.1%
s 2225
9.1%
r 2225
9.1%
n 2225
9.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 24475
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 4450
18.2%
e 4450
18.2%
t 2225
9.1%
o 2225
9.1%
u 2225
9.1%
h 2225
9.1%
s 2225
9.1%
r 2225
9.1%
n 2225
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 4450
18.2%
e 4450
18.2%
t 2225
9.1%
o 2225
9.1%
u 2225
9.1%
h 2225
9.1%
s 2225
9.1%
r 2225
9.1%
n 2225
9.1%

mobileDeviceInfo
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct34
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
Samsung SM-N975F Galaxy Note10+
1320 
Samsung SM-A505F Galaxy A50
520 
Huawei YAL-L21 Honor 20
150 
(not set)
136 
Xiaomi Redmi Note 9S
 
39
Other values (29)
196 

Length

Max length39
Median length31
Mean length27.399831
Min length9

Characters and Unicode

Total characters64691
Distinct characters59
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.4%

Sample

1st rowXiaomi Redmi 6A
2nd rowXiaomi Redmi 6A
3rd rowXiaomi Redmi 6A
4th row(not set)
5th row(not set)

Common Values

ValueCountFrequency (%)
Samsung SM-N975F Galaxy Note10+ 1320
55.9%
Samsung SM-A505F Galaxy A50 520
 
22.0%
Huawei YAL-L21 Honor 20 150
 
6.4%
(not set) 136
 
5.8%
Xiaomi Redmi Note 9S 39
 
1.7%
Samsung SM-G960F Galaxy S9 30
 
1.3%
Huawei MAR-LX2 Nova 4e 30
 
1.3%
Vivo 1819 V15 19
 
0.8%
Razer Phone 2 19
 
0.8%
Samsung SM-N770F Galaxy Note10 Lite 18
 
0.8%
Other values (24) 80
 
3.4%

Length

2023-06-07T21:51:54.862227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
samsung 1911
20.9%
galaxy 1911
20.9%
note10 1339
14.7%
sm-n975f 1320
14.5%
sm-a505f 520
 
5.7%
a50 520
 
5.7%
huawei 200
 
2.2%
yal-l21 150
 
1.6%
honor 150
 
1.6%
20 150
 
1.6%
Other values (73) 957
10.5%

Most occurring characters

ValueCountFrequency (%)
6767
 
10.5%
a 6059
 
9.4%
S 3891
 
6.0%
5 2908
 
4.5%
N 2801
 
4.3%
0 2640
 
4.1%
n 2232
 
3.5%
u 2116
 
3.3%
- 2111
 
3.3%
s 2058
 
3.2%
Other values (49) 31108
48.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28611
44.2%
Uppercase Letter 15027
23.2%
Decimal Number 10549
 
16.3%
Space Separator 6767
 
10.5%
Dash Punctuation 2111
 
3.3%
Math Symbol 1320
 
2.0%
Open Punctuation 145
 
0.2%
Close Punctuation 145
 
0.2%
Other Punctuation 16
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 3891
25.9%
N 2801
18.6%
M 1968
13.1%
G 1953
13.0%
F 1904
12.7%
A 1233
 
8.2%
L 396
 
2.6%
H 356
 
2.4%
Y 153
 
1.0%
R 100
 
0.7%
Other values (12) 272
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
a 6059
21.2%
n 2232
 
7.8%
u 2116
 
7.4%
s 2058
 
7.2%
m 2002
 
7.0%
o 1977
 
6.9%
x 1916
 
6.7%
l 1915
 
6.7%
y 1911
 
6.7%
g 1911
 
6.7%
Other values (11) 4514
15.8%
Decimal Number
ValueCountFrequency (%)
5 2908
27.6%
0 2640
25.0%
1 1592
15.1%
9 1478
14.0%
7 1368
13.0%
2 410
 
3.9%
6 62
 
0.6%
4 35
 
0.3%
8 33
 
0.3%
3 23
 
0.2%
Space Separator
ValueCountFrequency (%)
6767
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2111
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1320
100.0%
Open Punctuation
ValueCountFrequency (%)
( 145
100.0%
Close Punctuation
ValueCountFrequency (%)
) 145
100.0%
Other Punctuation
ValueCountFrequency (%)
. 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 43638
67.5%
Common 21053
32.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6059
 
13.9%
S 3891
 
8.9%
N 2801
 
6.4%
n 2232
 
5.1%
u 2116
 
4.8%
s 2058
 
4.7%
m 2002
 
4.6%
o 1977
 
4.5%
M 1968
 
4.5%
G 1953
 
4.5%
Other values (33) 16581
38.0%
Common
ValueCountFrequency (%)
6767
32.1%
5 2908
13.8%
0 2640
 
12.5%
- 2111
 
10.0%
1 1592
 
7.6%
9 1478
 
7.0%
7 1368
 
6.5%
+ 1320
 
6.3%
2 410
 
1.9%
( 145
 
0.7%
Other values (6) 314
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6767
 
10.5%
a 6059
 
9.4%
S 3891
 
6.0%
5 2908
 
4.5%
N 2801
 
4.3%
0 2640
 
4.1%
n 2232
 
3.5%
u 2116
 
3.3%
- 2111
 
3.3%
s 2058
 
3.2%
Other values (49) 31108
48.1%

mobileDeviceMarketingName
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct28
Distinct (%)1.3%
Missing136
Missing (%)5.8%
Memory size36.9 KiB
Galaxy Note10+
1320 
Galaxy A50
520 
Honor 20
150 
(not set)
 
79
Galaxy S9
 
30
Other values (23)
 
126

Length

Max length28
Median length14
Mean length12.164494
Min length2

Characters and Unicode

Total characters27066
Distinct characters45
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.4%

Sample

1st row(not set)
2nd row(not set)
3rd row(not set)
4th rowGalaxy J7 (2017)
5th rowGalaxy J7 (2017)

Common Values

ValueCountFrequency (%)
Galaxy Note10+ 1320
55.9%
Galaxy A50 520
 
22.0%
Honor 20 150
 
6.4%
(not set) 79
 
3.3%
Galaxy S9 30
 
1.3%
Nova 4e 30
 
1.3%
V15 19
 
0.8%
Galaxy Note10 Lite 18
 
0.8%
Nova 2 Lite 7
 
0.3%
P20 7
 
0.3%
Other values (18) 45
 
1.9%
(Missing) 136
 
5.8%

Length

2023-06-07T21:51:54.982853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
galaxy 1911
42.8%
note10 1339
30.0%
a50 520
 
11.7%
honor 150
 
3.4%
20 150
 
3.4%
not 79
 
1.8%
set 79
 
1.8%
nova 37
 
0.8%
s9 30
 
0.7%
4e 30
 
0.7%
Other values (27) 138
 
3.1%

Most occurring characters

ValueCountFrequency (%)
a 3867
14.3%
2238
 
8.3%
0 2029
 
7.5%
x 1916
 
7.1%
G 1911
 
7.1%
l 1911
 
7.1%
y 1911
 
7.1%
o 1780
 
6.6%
t 1537
 
5.7%
e 1501
 
5.5%
Other values (35) 6465
23.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14983
55.4%
Decimal Number 4224
 
15.6%
Uppercase Letter 4125
 
15.2%
Space Separator 2238
 
8.3%
Math Symbol 1320
 
4.9%
Open Punctuation 88
 
0.3%
Close Punctuation 88
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 1911
46.3%
N 1387
33.6%
A 523
 
12.7%
H 150
 
3.6%
L 31
 
0.8%
S 30
 
0.7%
V 22
 
0.5%
P 21
 
0.5%
M 13
 
0.3%
Z 10
 
0.2%
Other values (7) 27
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
a 3867
25.8%
x 1916
12.8%
l 1911
12.8%
y 1911
12.8%
o 1780
11.9%
t 1537
 
10.3%
e 1501
 
10.0%
n 245
 
1.6%
r 162
 
1.1%
s 79
 
0.5%
Other values (4) 74
 
0.5%
Decimal Number
ValueCountFrequency (%)
0 2029
48.0%
1 1380
32.7%
5 542
 
12.8%
2 173
 
4.1%
9 42
 
1.0%
4 30
 
0.7%
6 11
 
0.3%
7 9
 
0.2%
8 6
 
0.1%
3 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2238
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1320
100.0%
Open Punctuation
ValueCountFrequency (%)
( 88
100.0%
Close Punctuation
ValueCountFrequency (%)
) 88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19108
70.6%
Common 7958
29.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3867
20.2%
x 1916
10.0%
G 1911
10.0%
l 1911
10.0%
y 1911
10.0%
o 1780
9.3%
t 1537
 
8.0%
e 1501
 
7.9%
N 1387
 
7.3%
A 523
 
2.7%
Other values (21) 864
 
4.5%
Common
ValueCountFrequency (%)
2238
28.1%
0 2029
25.5%
1 1380
17.3%
+ 1320
16.6%
5 542
 
6.8%
2 173
 
2.2%
( 88
 
1.1%
) 88
 
1.1%
9 42
 
0.5%
4 30
 
0.4%
Other values (4) 28
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27066
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3867
14.3%
2238
 
8.3%
0 2029
 
7.5%
x 1916
 
7.1%
G 1911
 
7.1%
l 1911
 
7.1%
y 1911
 
7.1%
o 1780
 
6.6%
t 1537
 
5.7%
e 1501
 
5.5%
Other values (35) 6465
23.9%

screenResolution
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct22
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
360x760
1320 
412x892
520 
360x780
179 
1366x768
 
72
393x873
 
39
Other values (17)
231 

Length

Max length8
Median length7
Mean length7.0576027
Min length7

Characters and Unicode

Total characters16663
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.2%

Sample

1st row360x720
2nd row360x720
3rd row360x720
4th row1600x900
5th row1600x900

Common Values

ValueCountFrequency (%)
360x760 1320
55.9%
412x892 520
 
22.0%
360x780 179
 
7.6%
1366x768 72
 
3.0%
393x873 39
 
1.7%
360x740 36
 
1.5%
360x771 30
 
1.3%
1280x720 28
 
1.2%
1600x900 28
 
1.2%
480x854 19
 
0.8%
Other values (12) 90
 
3.8%

Length

2023-06-07T21:51:55.095295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
360x760 1320
55.9%
412x892 520
 
22.0%
360x780 179
 
7.6%
1366x768 72
 
3.0%
393x873 39
 
1.7%
360x740 36
 
1.5%
360x771 30
 
1.3%
1280x720 28
 
1.2%
1600x900 28
 
1.2%
480x854 19
 
0.8%
Other values (12) 90
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 3378
20.3%
6 3231
19.4%
x 2361
14.2%
3 1813
10.9%
7 1768
10.6%
2 1178
 
7.1%
8 889
 
5.3%
1 738
 
4.4%
4 649
 
3.9%
9 614
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14302
85.8%
Lowercase Letter 2361
 
14.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3378
23.6%
6 3231
22.6%
3 1813
12.7%
7 1768
12.4%
2 1178
 
8.2%
8 889
 
6.2%
1 738
 
5.2%
4 649
 
4.5%
9 614
 
4.3%
5 44
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
x 2361
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14302
85.8%
Latin 2361
 
14.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3378
23.6%
6 3231
22.6%
3 1813
12.7%
7 1768
12.4%
2 1178
 
8.2%
8 889
 
6.2%
1 738
 
5.2%
4 649
 
4.5%
9 614
 
4.3%
5 44
 
0.3%
Latin
ValueCountFrequency (%)
x 2361
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3378
20.3%
6 3231
19.4%
x 2361
14.2%
3 1813
10.9%
7 1768
10.6%
2 1178
 
7.1%
8 889
 
5.3%
1 738
 
4.4%
4 649
 
3.9%
9 614
 
3.7%

language
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
en-us
1295 
en-gb
1025 
ms
 
31
ms-my
 
8
en-my
 
1

Length

Max length5
Median length5
Mean length4.9606099
Min length2

Characters and Unicode

Total characters11712
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowen-us
2nd rowen-us
3rd rowen-us
4th rowen-us
5th rowen-us

Common Values

ValueCountFrequency (%)
en-us 1295
54.8%
en-gb 1025
43.4%
ms 31
 
1.3%
ms-my 8
 
0.3%
en-my 1
 
< 0.1%
en-sa 1
 
< 0.1%

Length

2023-06-07T21:51:55.213171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T21:51:55.340544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
en-us 1295
54.8%
en-gb 1025
43.4%
ms 31
 
1.3%
ms-my 8
 
0.3%
en-my 1
 
< 0.1%
en-sa 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
- 2330
19.9%
e 2322
19.8%
n 2322
19.8%
s 1335
11.4%
u 1295
11.1%
g 1025
8.8%
b 1025
8.8%
m 48
 
0.4%
y 9
 
0.1%
a 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9382
80.1%
Dash Punctuation 2330
 
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2322
24.7%
n 2322
24.7%
s 1335
14.2%
u 1295
13.8%
g 1025
10.9%
b 1025
10.9%
m 48
 
0.5%
y 9
 
0.1%
a 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 2330
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9382
80.1%
Common 2330
 
19.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2322
24.7%
n 2322
24.7%
s 1335
14.2%
u 1295
13.8%
g 1025
10.9%
b 1025
10.9%
m 48
 
0.5%
y 9
 
0.1%
a 1
 
< 0.1%
Common
ValueCountFrequency (%)
- 2330
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11712
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 2330
19.9%
e 2322
19.8%
n 2322
19.8%
s 1335
11.4%
u 1295
11.1%
g 1025
8.8%
b 1025
8.8%
m 48
 
0.4%
y 9
 
0.1%
a 1
 
< 0.1%

continent
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
Asia
2361 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters9444
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsia
2nd rowAsia
3rd rowAsia
4th rowAsia
5th rowAsia

Common Values

ValueCountFrequency (%)
Asia 2361
100.0%

Length

2023-06-07T21:51:55.446092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T21:51:55.538505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
asia 2361
100.0%

Most occurring characters

ValueCountFrequency (%)
A 2361
25.0%
s 2361
25.0%
i 2361
25.0%
a 2361
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7083
75.0%
Uppercase Letter 2361
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 2361
33.3%
i 2361
33.3%
a 2361
33.3%
Uppercase Letter
ValueCountFrequency (%)
A 2361
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9444
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2361
25.0%
s 2361
25.0%
i 2361
25.0%
a 2361
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9444
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2361
25.0%
s 2361
25.0%
i 2361
25.0%
a 2361
25.0%

subContinent
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
Southeast Asia
2334 
Eastern Asia
 
27

Length

Max length14
Median length14
Mean length13.977128
Min length12

Characters and Unicode

Total characters33000
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSoutheast Asia
2nd rowSoutheast Asia
3rd rowSoutheast Asia
4th rowSoutheast Asia
5th rowSoutheast Asia

Common Values

ValueCountFrequency (%)
Southeast Asia 2334
98.9%
Eastern Asia 27
 
1.1%

Length

2023-06-07T21:51:55.633343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T21:51:55.753692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
asia 2361
50.0%
southeast 2334
49.4%
eastern 27
 
0.6%

Most occurring characters

ValueCountFrequency (%)
a 4722
14.3%
s 4722
14.3%
t 4695
14.2%
e 2361
7.2%
2361
7.2%
A 2361
7.2%
i 2361
7.2%
S 2334
7.1%
o 2334
7.1%
u 2334
7.1%
Other values (4) 2415
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25917
78.5%
Uppercase Letter 4722
 
14.3%
Space Separator 2361
 
7.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4722
18.2%
s 4722
18.2%
t 4695
18.1%
e 2361
9.1%
i 2361
9.1%
o 2334
9.0%
u 2334
9.0%
h 2334
9.0%
r 27
 
0.1%
n 27
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
A 2361
50.0%
S 2334
49.4%
E 27
 
0.6%
Space Separator
ValueCountFrequency (%)
2361
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 30639
92.8%
Common 2361
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4722
15.4%
s 4722
15.4%
t 4695
15.3%
e 2361
7.7%
A 2361
7.7%
i 2361
7.7%
S 2334
7.6%
o 2334
7.6%
u 2334
7.6%
h 2334
7.6%
Other values (3) 81
 
0.3%
Common
ValueCountFrequency (%)
2361
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4722
14.3%
s 4722
14.3%
t 4695
14.2%
e 2361
7.2%
2361
7.2%
A 2361
7.2%
i 2361
7.2%
S 2334
7.1%
o 2334
7.1%
u 2334
7.1%
Other values (4) 2415
7.3%

country
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
Malaysia
2190 
Brunei
 
144
Hong Kong
 
27

Length

Max length9
Median length8
Mean length7.8894536
Min length6

Characters and Unicode

Total characters18627
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMalaysia
2nd rowMalaysia
3rd rowMalaysia
4th rowMalaysia
5th rowMalaysia

Common Values

ValueCountFrequency (%)
Malaysia 2190
92.8%
Brunei 144
 
6.1%
Hong Kong 27
 
1.1%

Length

2023-06-07T21:51:55.849252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T21:51:55.956210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
malaysia 2190
91.7%
brunei 144
 
6.0%
hong 27
 
1.1%
kong 27
 
1.1%

Most occurring characters

ValueCountFrequency (%)
a 6570
35.3%
i 2334
 
12.5%
M 2190
 
11.8%
l 2190
 
11.8%
y 2190
 
11.8%
s 2190
 
11.8%
n 198
 
1.1%
B 144
 
0.8%
r 144
 
0.8%
u 144
 
0.8%
Other values (6) 333
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16212
87.0%
Uppercase Letter 2388
 
12.8%
Space Separator 27
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6570
40.5%
i 2334
 
14.4%
l 2190
 
13.5%
y 2190
 
13.5%
s 2190
 
13.5%
n 198
 
1.2%
r 144
 
0.9%
u 144
 
0.9%
e 144
 
0.9%
o 54
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
M 2190
91.7%
B 144
 
6.0%
H 27
 
1.1%
K 27
 
1.1%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18600
99.9%
Common 27
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6570
35.3%
i 2334
 
12.5%
M 2190
 
11.8%
l 2190
 
11.8%
y 2190
 
11.8%
s 2190
 
11.8%
n 198
 
1.1%
B 144
 
0.8%
r 144
 
0.8%
u 144
 
0.8%
Other values (5) 306
 
1.6%
Common
ValueCountFrequency (%)
27
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18627
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6570
35.3%
i 2334
 
12.5%
M 2190
 
11.8%
l 2190
 
11.8%
y 2190
 
11.8%
s 2190
 
11.8%
n 198
 
1.1%
B 144
 
0.8%
r 144
 
0.8%
u 144
 
0.8%
Other values (6) 333
 
1.8%

region
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
Selangor
1409 
Perak
516 
Federal Territory of Kuala Lumpur
209 
Brunei-Muara District
144 
Johor
 
39
Other values (6)
 
44

Length

Max length33
Median length8
Mean length10.330792
Min length5

Characters and Unicode

Total characters24391
Distinct characters36
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowFederal Territory of Kuala Lumpur
2nd rowFederal Territory of Kuala Lumpur
3rd rowFederal Territory of Kuala Lumpur
4th rowSelangor
5th rowSelangor

Common Values

ValueCountFrequency (%)
Selangor 1409
59.7%
Perak 516
 
21.9%
Federal Territory of Kuala Lumpur 209
 
8.9%
Brunei-Muara District 144
 
6.1%
Johor 39
 
1.7%
(not set) 27
 
1.1%
Penang 7
 
0.3%
Kedah 4
 
0.2%
Labuan Federal Territory 4
 
0.2%
Sarawak 1
 
< 0.1%

Length

2023-06-07T21:51:56.064130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
selangor 1409
41.7%
perak 516
 
15.3%
federal 213
 
6.3%
territory 213
 
6.3%
of 209
 
6.2%
kuala 209
 
6.2%
lumpur 209
 
6.2%
brunei-muara 144
 
4.3%
district 144
 
4.3%
johor 39
 
1.2%
Other values (8) 72
 
2.1%

Most occurring characters

ValueCountFrequency (%)
r 3459
14.2%
a 2867
11.8%
e 2749
11.3%
o 1936
 
7.9%
l 1832
 
7.5%
n 1599
 
6.6%
g 1417
 
5.8%
S 1411
 
5.8%
1016
 
4.2%
u 919
 
3.8%
Other values (26) 5186
21.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19919
81.7%
Uppercase Letter 3258
 
13.4%
Space Separator 1016
 
4.2%
Dash Punctuation 144
 
0.6%
Open Punctuation 27
 
0.1%
Close Punctuation 27
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 3459
17.4%
a 2867
14.4%
e 2749
13.8%
o 1936
9.7%
l 1832
9.2%
n 1599
8.0%
g 1417
7.1%
u 919
 
4.6%
i 647
 
3.2%
t 555
 
2.8%
Other values (11) 1939
9.7%
Uppercase Letter
ValueCountFrequency (%)
S 1411
43.3%
P 523
 
16.1%
L 213
 
6.5%
K 213
 
6.5%
T 213
 
6.5%
F 213
 
6.5%
D 144
 
4.4%
M 144
 
4.4%
B 144
 
4.4%
J 39
 
1.2%
Space Separator
ValueCountFrequency (%)
1016
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 144
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23177
95.0%
Common 1214
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 3459
14.9%
a 2867
12.4%
e 2749
11.9%
o 1936
8.4%
l 1832
7.9%
n 1599
 
6.9%
g 1417
 
6.1%
S 1411
 
6.1%
u 919
 
4.0%
i 647
 
2.8%
Other values (22) 4341
18.7%
Common
ValueCountFrequency (%)
1016
83.7%
- 144
 
11.9%
( 27
 
2.2%
) 27
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24391
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 3459
14.2%
a 2867
11.8%
e 2749
11.3%
o 1936
 
7.9%
l 1832
 
7.5%
n 1599
 
6.6%
g 1417
 
5.8%
S 1411
 
5.8%
1016
 
4.2%
u 919
 
3.8%
Other values (26) 5186
21.3%

city
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct21
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
Telok Panglima Garang
1304 
Ipoh
516 
Kuala Lumpur
209 
Bandar Seri Begawan
144 
Shah Alam
 
33
Other values (16)
155 

Length

Max length21
Median length21
Mean length15.44388
Min length4

Characters and Unicode

Total characters36463
Distinct characters38
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowKuala Lumpur
2nd rowKuala Lumpur
3rd rowKuala Lumpur
4th rowBatu Caves
5th rowBatu Caves

Common Values

ValueCountFrequency (%)
Telok Panglima Garang 1304
55.2%
Ipoh 516
 
21.9%
Kuala Lumpur 209
 
8.9%
Bandar Seri Begawan 144
 
6.1%
Shah Alam 33
 
1.4%
Johor Bahru 31
 
1.3%
(not set) 28
 
1.2%
Petaling Jaya 22
 
0.9%
Rawang 17
 
0.7%
Banting 15
 
0.6%
Other values (11) 42
 
1.8%

Length

2023-06-07T21:51:56.191475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
telok 1304
23.3%
panglima 1304
23.3%
garang 1304
23.3%
ipoh 516
 
9.2%
kuala 209
 
3.7%
lumpur 209
 
3.7%
bandar 144
 
2.6%
seri 144
 
2.6%
begawan 144
 
2.6%
shah 33
 
0.6%
Other values (22) 296
 
5.3%

Most occurring characters

ValueCountFrequency (%)
a 6507
17.8%
3246
 
8.9%
n 3026
 
8.3%
l 2872
 
7.9%
g 2830
 
7.8%
o 1914
 
5.2%
r 1866
 
5.1%
e 1652
 
4.5%
m 1552
 
4.3%
i 1509
 
4.1%
Other values (28) 9489
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27610
75.7%
Uppercase Letter 5551
 
15.2%
Space Separator 3246
 
8.9%
Close Punctuation 28
 
0.1%
Open Punctuation 28
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6507
23.6%
n 3026
11.0%
l 2872
10.4%
g 2830
10.2%
o 1914
 
6.9%
r 1866
 
6.8%
e 1652
 
6.0%
m 1552
 
5.6%
i 1509
 
5.5%
k 1304
 
4.7%
Other values (12) 2578
 
9.3%
Uppercase Letter
ValueCountFrequency (%)
P 1342
24.2%
G 1304
23.5%
T 1304
23.5%
I 516
 
9.3%
B 345
 
6.2%
L 213
 
3.8%
K 213
 
3.8%
S 189
 
3.4%
J 65
 
1.2%
A 38
 
0.7%
Other values (3) 22
 
0.4%
Space Separator
ValueCountFrequency (%)
3246
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 33161
90.9%
Common 3302
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6507
19.6%
n 3026
 
9.1%
l 2872
 
8.7%
g 2830
 
8.5%
o 1914
 
5.8%
r 1866
 
5.6%
e 1652
 
5.0%
m 1552
 
4.7%
i 1509
 
4.6%
P 1342
 
4.0%
Other values (25) 8091
24.4%
Common
ValueCountFrequency (%)
3246
98.3%
) 28
 
0.8%
( 28
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6507
17.8%
3246
 
8.9%
n 3026
 
8.3%
l 2872
 
7.9%
g 2830
 
7.8%
o 1914
 
5.2%
r 1866
 
5.1%
e 1652
 
4.5%
m 1552
 
4.3%
i 1509
 
4.1%
Other values (28) 9489
26.0%

sec
Text

Distinct94
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
2023-06-07T21:51:56.413614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length137
Median length131
Mean length8.781025
Min length1

Characters and Unicode

Total characters20732
Distinct characters45
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)2.3%

Sample

1st row/video-nak-marah-tapi-kena-sabar-anak-diam-rupanya-masuk-sangkar-kucing
2nd row/suami-jual-isteri-peroleh-hampir-rm3k-sebulan-saya-sakit-hati-tengok-dia-lakukan-hubungan-intim-tapi
3rd row/suami-jual-isteri-peroleh-hampir-rm3k-sebulan-saya-sakit-hati-tengok-dia-lakukan-hubungan-intim-tapi
4th row/rakyat-malaysia-maut-dibunuh-di-australia-didakwa-jadi-penjaga-1000-pokok-ganja-bagi-kumpulan-jenayah
5th row/datuk-74-tahun-maut-selepas-tunai-hajat-di-rumah-pelacuran-polis-jumpa-ubat-kuat
ValueCountFrequency (%)
870
36.7%
news 844
35.6%
world 219
 
9.2%
lifestyle 116
 
4.9%
sports 61
 
2.6%
business 50
 
2.1%
opinion 45
 
1.9%
my 28
 
1.2%
kantoi-masuk-bilik-tidur-anak-tiri-isteri-tetak-kemaluan-suami-lepas-tengok-rakaman-web-cam 8
 
0.3%
wanita-dirogol-6-lelaki-di-hadapan-suami-2-orang-anak-ketika-dalam-perjalanan-pulang-dari-kebun 7
 
0.3%
Other values (82) 121
 
5.1%
2023-06-07T21:51:56.817594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 3686
17.8%
e 1804
 
8.7%
a 1716
 
8.3%
n 1702
 
8.2%
s 1680
 
8.1%
- 1482
 
7.1%
w 1124
 
5.4%
i 947
 
4.6%
l 778
 
3.8%
r 735
 
3.5%
Other values (35) 5078
24.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15440
74.5%
Other Punctuation 3707
 
17.9%
Dash Punctuation 1482
 
7.1%
Decimal Number 68
 
0.3%
Math Symbol 21
 
0.1%
Space Separator 8
 
< 0.1%
Uppercase Letter 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1804
11.7%
a 1716
11.1%
n 1702
11.0%
s 1680
10.9%
w 1124
 
7.3%
i 947
 
6.1%
l 778
 
5.0%
r 735
 
4.8%
t 684
 
4.4%
o 595
 
3.9%
Other values (15) 3675
23.8%
Decimal Number
ValueCountFrequency (%)
2 18
26.5%
1 8
11.8%
6 8
11.8%
4 8
11.8%
7 8
11.8%
0 7
 
10.3%
3 7
 
10.3%
5 3
 
4.4%
8 1
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
P 2
33.3%
I 1
16.7%
N 1
16.7%
K 1
16.7%
J 1
16.7%
Other Punctuation
ValueCountFrequency (%)
/ 3686
99.4%
? 18
 
0.5%
& 3
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 1482
100.0%
Math Symbol
ValueCountFrequency (%)
= 21
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15446
74.5%
Common 5286
 
25.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1804
11.7%
a 1716
11.1%
n 1702
11.0%
s 1680
10.9%
w 1124
 
7.3%
i 947
 
6.1%
l 778
 
5.0%
r 735
 
4.8%
t 684
 
4.4%
o 595
 
3.9%
Other values (20) 3681
23.8%
Common
ValueCountFrequency (%)
/ 3686
69.7%
- 1482
28.0%
= 21
 
0.4%
? 18
 
0.3%
2 18
 
0.3%
8
 
0.2%
1 8
 
0.2%
6 8
 
0.2%
4 8
 
0.2%
7 8
 
0.2%
Other values (5) 21
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 3686
17.8%
e 1804
 
8.7%
a 1716
 
8.3%
n 1702
 
8.2%
s 1680
 
8.1%
- 1482
 
7.1%
w 1124
 
5.4%
i 947
 
4.6%
l 778
 
3.8%
r 735
 
3.5%
Other values (35) 5078
24.5%

sub_sec
Categorical

Distinct27
Distinct (%)2.0%
Missing1036
Missing (%)43.9%
Memory size36.9 KiB
/nation/
475 
/politics/
200 
/world/
196 
/crime-courts/
151 
/groove/
75 
Other values (22)
228 

Length

Max length26
Median length14
Mean length9.0581132
Min length1

Characters and Unicode

Total characters12002
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st row/nation/
2nd row/nation/
3rd row/news/
4th row/badminton/
5th row/region/

Common Values

ValueCountFrequency (%)
/nation/ 475
20.1%
/politics/ 200
 
8.5%
/world/ 196
 
8.3%
/crime-courts/ 151
 
6.4%
/groove/ 75
 
3.2%
/2022/ 45
 
1.9%
/badminton/ 36
 
1.5%
/leaders/ 24
 
1.0%
/football/ 15
 
0.6%
/letters/ 14
 
0.6%
Other values (17) 94
 
4.0%
(Missing) 1036
43.9%

Length

2023-06-07T21:51:56.958347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nation 478
36.1%
politics 200
15.1%
world 196
14.8%
crime-courts 151
 
11.4%
groove 75
 
5.7%
2022 45
 
3.4%
badminton 37
 
2.8%
leaders 24
 
1.8%
football 15
 
1.1%
letters 14
 
1.1%
Other values (15) 90
 
6.8%

Most occurring characters

ValueCountFrequency (%)
/ 2643
22.0%
o 1289
10.7%
i 1143
9.5%
n 1098
9.1%
t 933
 
7.8%
r 636
 
5.3%
a 583
 
4.9%
c 529
 
4.4%
l 500
 
4.2%
s 465
 
3.9%
Other values (17) 2183
18.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8997
75.0%
Other Punctuation 2643
 
22.0%
Dash Punctuation 182
 
1.5%
Decimal Number 180
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1289
14.3%
i 1143
12.7%
n 1098
12.2%
t 933
10.4%
r 636
7.1%
a 583
6.5%
c 529
 
5.9%
l 500
 
5.6%
s 465
 
5.2%
e 379
 
4.2%
Other values (13) 1442
16.0%
Decimal Number
ValueCountFrequency (%)
2 135
75.0%
0 45
 
25.0%
Other Punctuation
ValueCountFrequency (%)
/ 2643
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 182
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8997
75.0%
Common 3005
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1289
14.3%
i 1143
12.7%
n 1098
12.2%
t 933
10.4%
r 636
7.1%
a 583
6.5%
c 529
 
5.9%
l 500
 
5.6%
s 465
 
5.2%
e 379
 
4.2%
Other values (13) 1442
16.0%
Common
ValueCountFrequency (%)
/ 2643
88.0%
- 182
 
6.1%
2 135
 
4.5%
0 45
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12002
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 2643
22.0%
o 1289
10.7%
i 1143
9.5%
n 1098
9.1%
t 933
 
7.8%
r 636
 
5.3%
a 583
 
4.9%
c 529
 
4.4%
l 500
 
4.2%
s 465
 
3.9%
Other values (17) 2183
18.2%
Distinct453
Distinct (%)19.2%
Missing1
Missing (%)< 0.1%
Memory size36.9 KiB
2023-06-07T21:51:57.540979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length136
Median length125
Mean length43.044068
Min length15

Characters and Unicode

Total characters101584
Distinct characters84
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique125 ?
Unique (%)5.3%

Sample

1st row[VIDEO] Nak Marah Tapi Kena Sabar, Anak Diam Rupanya Masuk Sangkar Kucing
2nd rowSuami Jual Isteri, Peroleh Hampir RM3k Sebulan – 'Saya Sakit Hati Tengok Dia Lakukan Hubungan Intim Tapi..'
3rd rowSuami Jual Isteri, Peroleh Hampir RM3k Sebulan – 'Saya Sakit Hati Tengok Dia Lakukan Hubungan Intim Tapi..'
4th rowRakyat Malaysia Maut 'Dibunuh' Di Australia, Didakwa Jadi Penjaga 1,000 Pokok Ganja Bagi Kumpulan Jenayah
5th rowDatuk 74 Tahun Maut Selepas Tunai 'Hajat' Di Rumah Pelacuran, Polis Jumpa Ubat Kuat
ValueCountFrequency (%)
new 978
 
5.9%
times 954
 
5.8%
straits 938
 
5.7%
to 389
 
2.4%
of 252
 
1.5%
in 234
 
1.4%
for 194
 
1.2%
says 149
 
0.9%
114
 
0.7%
on 90
 
0.5%
Other values (2397) 12175
73.9%
2023-06-07T21:51:58.340265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14107
13.9%
e 8489
 
8.4%
a 7813
 
7.7%
i 7314
 
7.2%
s 6576
 
6.5%
t 6181
 
6.1%
r 5374
 
5.3%
n 4918
 
4.8%
o 4886
 
4.8%
l 2842
 
2.8%
Other values (74) 33084
32.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 75108
73.9%
Space Separator 14107
 
13.9%
Uppercase Letter 9330
 
9.2%
Other Punctuation 1609
 
1.6%
Decimal Number 872
 
0.9%
Dash Punctuation 403
 
0.4%
Open Punctuation 59
 
0.1%
Close Punctuation 59
 
0.1%
Math Symbol 32
 
< 0.1%
Currency Symbol 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8489
11.3%
a 7813
10.4%
i 7314
9.7%
s 6576
 
8.8%
t 6181
 
8.2%
r 5374
 
7.2%
n 4918
 
6.5%
o 4886
 
6.5%
l 2842
 
3.8%
m 2572
 
3.4%
Other values (17) 18143
24.2%
Uppercase Letter
ValueCountFrequency (%)
S 1685
18.1%
T 1487
15.9%
N 1212
13.0%
M 650
 
7.0%
A 494
 
5.3%
P 351
 
3.8%
B 336
 
3.6%
R 327
 
3.5%
C 326
 
3.5%
K 303
 
3.2%
Other values (16) 2159
23.1%
Other Punctuation
ValueCountFrequency (%)
' 665
41.3%
, 475
29.5%
: 249
 
15.5%
# 68
 
4.2%
& 58
 
3.6%
. 39
 
2.4%
" 18
 
1.1%
! 18
 
1.1%
? 10
 
0.6%
; 4
 
0.2%
Other values (2) 5
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 210
24.1%
2 123
14.1%
0 118
13.5%
9 112
12.8%
5 87
10.0%
4 54
 
6.2%
3 53
 
6.1%
7 53
 
6.1%
6 44
 
5.0%
8 18
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 365
90.6%
– 38
 
9.4%
Open Punctuation
ValueCountFrequency (%)
[ 55
93.2%
( 4
 
6.8%
Close Punctuation
ValueCountFrequency (%)
] 55
93.2%
) 4
 
6.8%
Space Separator
ValueCountFrequency (%)
14107
100.0%
Math Symbol
ValueCountFrequency (%)
| 32
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 84438
83.1%
Common 17146
 
16.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8489
 
10.1%
a 7813
 
9.3%
i 7314
 
8.7%
s 6576
 
7.8%
t 6181
 
7.3%
r 5374
 
6.4%
n 4918
 
5.8%
o 4886
 
5.8%
l 2842
 
3.4%
m 2572
 
3.0%
Other values (43) 27473
32.5%
Common
ValueCountFrequency (%)
14107
82.3%
' 665
 
3.9%
, 475
 
2.8%
- 365
 
2.1%
: 249
 
1.5%
1 210
 
1.2%
2 123
 
0.7%
0 118
 
0.7%
9 112
 
0.7%
5 87
 
0.5%
Other values (21) 635
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101542
> 99.9%
Punctuation 40
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14107
13.9%
e 8489
 
8.4%
a 7813
 
7.7%
i 7314
 
7.2%
s 6576
 
6.5%
t 6181
 
6.1%
r 5374
 
5.3%
n 4918
 
4.8%
o 4886
 
4.8%
l 2842
 
2.8%
Other values (71) 33042
32.5%
Punctuation
ValueCountFrequency (%)
– 38
95.0%
… 2
 
5.0%
None
ValueCountFrequency (%)
é 2
100.0%

url
Text

Distinct476
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
2023-06-07T21:51:59.087453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length156
Median length141
Mean length71.253283
Min length20

Characters and Unicode

Total characters168229
Distinct characters50
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique144 ?
Unique (%)6.1%

Sample

1st rowhttps://ohbulan.com/video-nak-marah-tapi-kena-sabar-anak-diam-rupanya-masuk-sangkar-kucing
2nd rowhttps://ohbulan.com/suami-jual-isteri-peroleh-hampir-rm3k-sebulan-saya-sakit-hati-tengok-dia-lakukan-hubungan-intim-tapi
3rd rowhttps://ohbulan.com/suami-jual-isteri-peroleh-hampir-rm3k-sebulan-saya-sakit-hati-tengok-dia-lakukan-hubungan-intim-tapi
4th rowhttps://ohbulan.com/rakyat-malaysia-maut-dibunuh-di-australia-didakwa-jadi-penjaga-1000-pokok-ganja-bagi-kumpulan-jenayah
5th rowhttps://ohbulan.com/datuk-74-tahun-maut-selepas-tunai-hajat-di-rumah-pelacuran-polis-jumpa-ubat-kuat
ValueCountFrequency (%)
https://www.nst.com.my 866
36.5%
https://www.nst.com.my/news/politics/2022/03/779651/johor-polls-seven-party-leaders-bite-dust 19
 
0.8%
https://www.nst.com.my/lifestyle 16
 
0.7%
https://www.nst.com.my/world/world/2022/03/780026/my-life-will-always-be-devoted-service-uks-queen-elizabeth-says 12
 
0.5%
https://www.nst.com.my/news/politics/2022/03/781105/zahid-reiterates-umnos-stand-call-early-ge 12
 
0.5%
https://www.nst.com.my/world 12
 
0.5%
https://www.nst.com.my/news/nation/2022/03/783468/75-year-old-covid-19-positive-man-falls-death-condo 11
 
0.5%
https://www.nst.com.my/news/crime-courts/2022/03/783459/elderly-woman-robbed-front-sons-house-bandar-tun-razak 11
 
0.5%
https://www.nst.com.my/news/nation/2022/03/783631/mysejahtera-app-still-under-govt-not-sold-private-entity 11
 
0.5%
https://www.nst.com.my/lifestyle/groove/2022/04/785307/showbiz-soo-wincci-first-miss-world-malaysia-who-also-associate 11
 
0.5%
Other values (470) 1392
58.7%
2023-06-07T21:51:59.949117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 13551
 
8.1%
t 12173
 
7.2%
s 11522
 
6.8%
- 10646
 
6.3%
n 9194
 
5.5%
w 8777
 
5.2%
e 8080
 
4.8%
o 8065
 
4.8%
a 7842
 
4.7%
m 7007
 
4.2%
Other values (40) 71372
42.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 118466
70.4%
Other Punctuation 22734
 
13.5%
Decimal Number 16344
 
9.7%
Dash Punctuation 10646
 
6.3%
Math Symbol 21
 
< 0.1%
Space Separator 12
 
< 0.1%
Uppercase Letter 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 12173
 
10.3%
s 11522
 
9.7%
n 9194
 
7.8%
w 8777
 
7.4%
e 8080
 
6.8%
o 8065
 
6.8%
a 7842
 
6.6%
m 7007
 
5.9%
i 6556
 
5.5%
r 5327
 
4.5%
Other values (17) 33923
28.6%
Decimal Number
ValueCountFrequency (%)
2 4616
28.2%
0 3236
19.8%
7 1937
11.9%
3 1467
 
9.0%
8 1365
 
8.4%
4 942
 
5.8%
1 830
 
5.1%
6 660
 
4.0%
9 652
 
4.0%
5 639
 
3.9%
Other Punctuation
ValueCountFrequency (%)
/ 13551
59.6%
. 6801
29.9%
: 2361
 
10.4%
? 18
 
0.1%
& 3
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
P 2
33.3%
I 1
16.7%
N 1
16.7%
K 1
16.7%
J 1
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 10646
100.0%
Math Symbol
ValueCountFrequency (%)
= 21
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 118472
70.4%
Common 49757
29.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 12173
 
10.3%
s 11522
 
9.7%
n 9194
 
7.8%
w 8777
 
7.4%
e 8080
 
6.8%
o 8065
 
6.8%
a 7842
 
6.6%
m 7007
 
5.9%
i 6556
 
5.5%
r 5327
 
4.5%
Other values (22) 33929
28.6%
Common
ValueCountFrequency (%)
/ 13551
27.2%
- 10646
21.4%
. 6801
13.7%
2 4616
 
9.3%
0 3236
 
6.5%
: 2361
 
4.7%
7 1937
 
3.9%
3 1467
 
2.9%
8 1365
 
2.7%
4 942
 
1.9%
Other values (8) 2835
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 168227
> 99.9%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 13551
 
8.1%
t 12173
 
7.2%
s 11522
 
6.8%
- 10646
 
6.3%
n 9194
 
5.5%
w 8777
 
5.2%
e 8080
 
4.8%
o 8065
 
4.8%
a 7842
 
4.7%
m 7007
 
4.2%
Other values (39) 71370
42.4%
None
ValueCountFrequency (%)
é 2
100.0%

onPageTime
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct856
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258911.15
Minimum0
Maximum2448447
Zeros74
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size36.9 KiB
2023-06-07T21:52:00.111064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile221
Q12244
median125599
Q3334256
95-th percentile1269478
Maximum2448447
Range2448447
Interquartile range (IQR)332012

Descriptive statistics

Standard deviation385334.92
Coefficient of variation (CV)1.4882902
Kurtosis6.6552911
Mean258911.15
Median Absolute Deviation (MAD)124672
Skewness2.4589022
Sum6.1128922 × 108
Variance1.48483 × 1011
MonotonicityNot monotonic
2023-06-07T21:52:00.242481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74
 
3.1%
281 8
 
0.3%
243 6
 
0.3%
476 6
 
0.3%
333 6
 
0.3%
238 6
 
0.3%
320 6
 
0.3%
1009 5
 
0.2%
723 5
 
0.2%
32118 5
 
0.2%
Other values (846) 2234
94.6%
ValueCountFrequency (%)
0 74
3.1%
34 4
 
0.2%
76 1
 
< 0.1%
81 1
 
< 0.1%
91 1
 
< 0.1%
92 4
 
0.2%
105 1
 
< 0.1%
118 3
 
0.1%
120 1
 
< 0.1%
124 1
 
< 0.1%
ValueCountFrequency (%)
2448447 1
 
< 0.1%
2448060 1
 
< 0.1%
2358824 1
 
< 0.1%
2323488 2
0.1%
2322735 1
 
< 0.1%
2307199 1
 
< 0.1%
2267465 1
 
< 0.1%
1822646 4
0.2%
1820458 1
 
< 0.1%
1796249 2
0.1%

nextTime
Real number (ℝ)

Distinct936
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean384208.73
Minimum4553
Maximum3805761
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.9 KiB
2023-06-07T21:52:00.383693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4553
5-th percentile18576
Q1104597
median232139
Q3495067
95-th percentile1462459
Maximum3805761
Range3801208
Interquartile range (IQR)390470

Descriptive statistics

Standard deviation440470.47
Coefficient of variation (CV)1.1464353
Kurtosis6.7006643
Mean384208.73
Median Absolute Deviation (MAD)166624
Skewness2.3190617
Sum9.0711681 × 108
Variance1.9401423 × 1011
MonotonicityNot monotonic
2023-06-07T21:52:00.527026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16519 5
 
0.2%
32118 5
 
0.2%
173514 4
 
0.2%
419793 4
 
0.2%
47997 4
 
0.2%
222380 4
 
0.2%
230646 4
 
0.2%
12699 4
 
0.2%
139443 4
 
0.2%
169503 4
 
0.2%
Other values (926) 2319
98.2%
ValueCountFrequency (%)
4553 4
0.2%
6208 2
0.1%
6454 1
 
< 0.1%
7330 1
 
< 0.1%
7811 3
0.1%
7914 1
 
< 0.1%
8109 1
 
< 0.1%
8140 4
0.2%
10033 4
0.2%
10303 3
0.1%
ValueCountFrequency (%)
3805761 1
< 0.1%
3183200 2
0.1%
2526602 2
0.1%
2490833 1
< 0.1%
2467467 1
< 0.1%
2448447 1
< 0.1%
2448060 1
< 0.1%
2358824 1
< 0.1%
2335052 2
0.1%
2323488 2
0.1%

user_active_days
Real number (ℝ)

Distinct20
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.268107
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.9 KiB
2023-06-07T21:52:00.663542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q124
median24
Q325
95-th percentile30
Maximum30
Range29
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.3590083
Coefficient of variation (CV)0.2732929
Kurtosis2.5106901
Mean23.268107
Median Absolute Deviation (MAD)0
Skewness-1.6591438
Sum54936
Variance40.436987
MonotonicityNot monotonic
2023-06-07T21:52:00.772402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
24 1296
54.9%
30 519
22.0%
25 144
 
6.1%
7 73
 
3.1%
5 64
 
2.7%
14 53
 
2.2%
15 32
 
1.4%
19 30
 
1.3%
23 30
 
1.3%
11 20
 
0.8%
Other values (10) 100
 
4.2%
ValueCountFrequency (%)
1 5
 
0.2%
2 14
 
0.6%
4 9
 
0.4%
5 64
2.7%
6 5
 
0.2%
7 73
3.1%
8 11
 
0.5%
9 7
 
0.3%
10 5
 
0.2%
11 20
 
0.8%
ValueCountFrequency (%)
30 519
22.0%
25 144
 
6.1%
24 1296
54.9%
23 30
 
1.3%
22 18
 
0.8%
19 30
 
1.3%
18 19
 
0.8%
15 32
 
1.4%
14 53
 
2.2%
13 7
 
0.3%

total_visit_counts
Real number (ℝ)

Distinct44
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2501.7522
Minimum1
Maximum3328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.9 KiB
2023-06-07T21:52:00.907667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile232
Q12041
median3328
Q33328
95-th percentile3328
Maximum3328
Range3327
Interquartile range (IQR)1287

Descriptive statistics

Standard deviation1038.8399
Coefficient of variation (CV)0.4152449
Kurtosis-0.36465621
Mean2501.7522
Median Absolute Deviation (MAD)0
Skewness-0.92759625
Sum5906637
Variance1079188.3
MonotonicityNot monotonic
2023-06-07T21:52:01.048071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
3328 1296
54.9%
2041 519
22.0%
1755 144
 
6.1%
634 67
 
2.8%
868 37
 
1.6%
2534 30
 
1.3%
1144 30
 
1.3%
196 28
 
1.2%
760 25
 
1.1%
129 24
 
1.0%
Other values (34) 161
 
6.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
8 2
0.1%
10 1
 
< 0.1%
12 3
0.1%
15 1
 
< 0.1%
19 4
0.2%
30 1
 
< 0.1%
43 3
0.1%
48 2
0.1%
57 1
 
< 0.1%
ValueCountFrequency (%)
3328 1296
54.9%
2534 30
 
1.3%
2116 6
 
0.3%
2041 519
22.0%
1755 144
 
6.1%
1144 30
 
1.3%
1123 5
 
0.2%
1023 2
 
0.1%
876 19
 
0.8%
868 37
 
1.6%

total_unique_session
Real number (ℝ)

Distinct23
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.756883
Minimum1
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.9 KiB
2023-06-07T21:52:01.187923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q171
median71
Q373
95-th percentile79
Maximum79
Range78
Interquartile range (IQR)2

Descriptive statistics

Standard deviation21.913316
Coefficient of variation (CV)0.34370118
Kurtosis1.7908575
Mean63.756883
Median Absolute Deviation (MAD)0
Skewness-1.8514461
Sum150530
Variance480.19341
MonotonicityNot monotonic
2023-06-07T21:52:01.303882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
71 1296
54.9%
79 519
22.0%
73 144
 
6.1%
7 72
 
3.0%
19 48
 
2.0%
5 40
 
1.7%
24 37
 
1.6%
11 31
 
1.3%
21 30
 
1.3%
53 30
 
1.3%
Other values (13) 114
 
4.8%
ValueCountFrequency (%)
1 5
 
0.2%
2 10
 
0.4%
3 4
 
0.2%
4 2
 
0.1%
5 40
1.7%
7 72
3.0%
8 5
 
0.2%
9 6
 
0.3%
10 7
 
0.3%
11 31
1.3%
ValueCountFrequency (%)
79 519
22.0%
73 144
 
6.1%
71 1296
54.9%
53 30
 
1.3%
35 18
 
0.8%
27 19
 
0.8%
24 37
 
1.6%
21 30
 
1.3%
19 48
 
2.0%
18 1
 
< 0.1%

total_time_spent
Real number (ℝ)

Distinct45
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166214.66
Minimum54
Maximum243007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.9 KiB
2023-06-07T21:52:01.453776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile4496
Q197538
median243007
Q3243007
95-th percentile243007
Maximum243007
Range242953
Interquartile range (IQR)145469

Descriptive statistics

Standard deviation89128.94
Coefficient of variation (CV)0.53622792
Kurtosis-1.315649
Mean166214.66
Median Absolute Deviation (MAD)0
Skewness-0.5107787
Sum3.9243281 × 108
Variance7.9439679 × 109
MonotonicityNot monotonic
2023-06-07T21:52:01.613560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
243007 1296
54.9%
97538 519
22.0%
108436 144
 
6.1%
101631 67
 
2.8%
18091 37
 
1.6%
32035 30
 
1.3%
4998 30
 
1.3%
45534 28
 
1.2%
4496 25
 
1.1%
2027 24
 
1.0%
Other values (35) 161
 
6.8%
ValueCountFrequency (%)
54 2
0.1%
120 1
< 0.1%
178 1
< 0.1%
210 1
< 0.1%
236 1
< 0.1%
294 2
0.1%
375 1
< 0.1%
400 1
< 0.1%
446 1
< 0.1%
493 1
< 0.1%
ValueCountFrequency (%)
243007 1296
54.9%
108436 144
 
6.1%
101631 67
 
2.8%
97538 519
22.0%
45534 28
 
1.2%
32035 30
 
1.3%
24783 19
 
0.8%
18091 37
 
1.6%
14017 7
 
0.3%
11206 19
 
0.8%

time_spent_per_session
Real number (ℝ)

Distinct256
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean625.62812
Minimum8
Maximum3806
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.9 KiB
2023-06-07T21:52:01.751961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile98
Q1254
median496
Q3764
95-th percentile1751
Maximum3806
Range3798
Interquartile range (IQR)510

Descriptive statistics

Standard deviation521.11068
Coefficient of variation (CV)0.83293999
Kurtosis3.8946291
Mean625.62812
Median Absolute Deviation (MAD)263
Skewness1.7460773
Sum1477108
Variance271556.35
MonotonicityNot monotonic
2023-06-07T21:52:01.892962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
922 56
 
2.4%
1903 56
 
2.4%
757 56
 
2.4%
670 55
 
2.3%
862 48
 
2.0%
672 45
 
1.9%
944 44
 
1.9%
412 36
 
1.5%
741 36
 
1.5%
596 36
 
1.5%
Other values (246) 1893
80.2%
ValueCountFrequency (%)
8 4
0.2%
12 1
 
< 0.1%
13 1
 
< 0.1%
18 2
0.1%
22 1
 
< 0.1%
29 1
 
< 0.1%
30 2
0.1%
31 1
 
< 0.1%
34 1
 
< 0.1%
41 4
0.2%
ValueCountFrequency (%)
3806 2
 
0.1%
3183 6
 
0.3%
2527 4
 
0.2%
2525 15
 
0.6%
2491 4
 
0.2%
2384 6
 
0.3%
2088 4
 
0.2%
2065 6
 
0.3%
1985 2
 
0.1%
1903 56
2.4%

timeOnPage
Real number (ℝ)

Distinct936
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125297.58
Minimum213
Maximum2357471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.9 KiB
2023-06-07T21:52:02.042010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum213
5-th percentile7604
Q124006
median59645
Q3125285
95-th percentile462085
Maximum2357471
Range2357258
Interquartile range (IQR)101279

Descriptive statistics

Standard deviation230478.99
Coefficient of variation (CV)1.8394528
Kurtosis27.023376
Mean125297.58
Median Absolute Deviation (MAD)42827
Skewness4.7818016
Sum2.958276 × 108
Variance5.3120564 × 1010
MonotonicityNot monotonic
2023-06-07T21:52:02.172583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56334 8
 
0.3%
90143 4
 
0.2%
94783 4
 
0.2%
8266 4
 
0.2%
12366 4
 
0.2%
126744 4
 
0.2%
30060 4
 
0.2%
222992 4
 
0.2%
27298 4
 
0.2%
74680 4
 
0.2%
Other values (926) 2317
98.1%
ValueCountFrequency (%)
213 1
< 0.1%
290 1
< 0.1%
293 1
< 0.1%
299 1
< 0.1%
387 1
< 0.1%
425 1
< 0.1%
540 1
< 0.1%
632 1
< 0.1%
936 1
< 0.1%
1346 1
< 0.1%
ValueCountFrequency (%)
2357471 1
 
< 0.1%
1817873 1
 
< 0.1%
1796085 2
0.1%
1748726 2
0.1%
1739236 1
 
< 0.1%
1711007 1
 
< 0.1%
1702775 3
0.1%
1692451 2
0.1%
1684888 2
0.1%
1680524 2
0.1%

GAClientId
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct42
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
1990207551.1572628814
1320 
1210251251.1572441982
519 
119463514.1596533239
144 
345467720.1512180611
 
68
1463832559.1594111754
 
39
Other values (37)
271 

Length

Max length68
Median length21
Mean length21.358746
Min length19

Characters and Unicode

Total characters50428
Distinct characters64
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.5%

Sample

1st rowamp-35vZcqfyySaxus6_748cgw
2nd rowamp-35vZcqfyySaxus6_748cgw
3rd rowamp-35vZcqfyySaxus6_748cgw
4th row269799904.1574837317
5th row269799904.1574837317

Common Values

ValueCountFrequency (%)
1990207551.1572628814 1320
55.9%
1210251251.1572441982 519
 
22.0%
119463514.1596533239 144
 
6.1%
345467720.1512180611 68
 
2.9%
1463832559.1594111754 39
 
1.7%
794896869.1530833982 30
 
1.3%
425114537.1571194343 30
 
1.3%
803641785.1607062510 28
 
1.2%
161282953.1586164891 25
 
1.1%
956503047.1586396037 19
 
0.8%
Other values (32) 139
 
5.9%

Length

2023-06-07T21:52:02.301035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1990207551.1572628814 1320
55.9%
1210251251.1572441982 519
 
22.0%
119463514.1596533239 144
 
6.1%
345467720.1512180611 68
 
2.9%
1463832559.1594111754 39
 
1.7%
794896869.1530833982 30
 
1.3%
425114537.1571194343 30
 
1.3%
803641785.1607062510 28
 
1.2%
161282953.1586164891 25
 
1.1%
2123807980.1587881577 19
 
0.8%
Other values (32) 139
 
5.9%

Most occurring characters

ValueCountFrequency (%)
1 10260
20.3%
2 7202
14.3%
5 6829
13.5%
9 4115
8.2%
7 3870
 
7.7%
8 3831
 
7.6%
0 3636
 
7.2%
4 3265
 
6.5%
. 2328
 
4.6%
6 2270
 
4.5%
Other values (54) 2822
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46504
92.2%
Other Punctuation 2328
 
4.6%
Lowercase Letter 873
 
1.7%
Uppercase Letter 642
 
1.3%
Dash Punctuation 52
 
0.1%
Connector Punctuation 29
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 82
12.8%
B 68
 
10.6%
V 61
 
9.5%
Z 50
 
7.8%
L 48
 
7.5%
C 41
 
6.4%
D 29
 
4.5%
H 28
 
4.4%
T 27
 
4.2%
J 26
 
4.0%
Other values (16) 182
28.3%
Lowercase Letter
ValueCountFrequency (%)
p 93
 
10.7%
v 72
 
8.2%
x 69
 
7.9%
m 68
 
7.8%
a 65
 
7.4%
z 63
 
7.2%
n 59
 
6.8%
q 50
 
5.7%
k 39
 
4.5%
w 33
 
3.8%
Other values (15) 262
30.0%
Decimal Number
ValueCountFrequency (%)
1 10260
22.1%
2 7202
15.5%
5 6829
14.7%
9 4115
8.8%
7 3870
 
8.3%
8 3831
 
8.2%
0 3636
 
7.8%
4 3265
 
7.0%
6 2270
 
4.9%
3 1226
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 2328
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 52
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48913
97.0%
Latin 1515
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 93
 
6.1%
O 82
 
5.4%
v 72
 
4.8%
x 69
 
4.6%
B 68
 
4.5%
m 68
 
4.5%
a 65
 
4.3%
z 63
 
4.2%
V 61
 
4.0%
n 59
 
3.9%
Other values (41) 815
53.8%
Common
ValueCountFrequency (%)
1 10260
21.0%
2 7202
14.7%
5 6829
14.0%
9 4115
8.4%
7 3870
 
7.9%
8 3831
 
7.8%
0 3636
 
7.4%
4 3265
 
6.7%
. 2328
 
4.8%
6 2270
 
4.6%
Other values (3) 1307
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10260
20.3%
2 7202
14.3%
5 6829
13.5%
9 4115
8.2%
7 3870
 
7.7%
8 3831
 
7.6%
0 3636
 
7.2%
4 3265
 
6.5%
. 2328
 
4.6%
6 2270
 
4.5%
Other values (54) 2822
 
5.6%

cookie_id
Categorical

Distinct49
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
7T/yP+n/OW8uEJB3
330 
VHciCTdcN8uLLIy0
330 
WF8pC3HscIBm6Oq-
330 
Z4WqEac4BZQhQ/eJ
330 
j1hPDn/zHYJDJtt1
173 
Other values (44)
868 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters37776
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.5%

Sample

1st rowIhHSD5tP1NnqeA1g
2nd rowIhHSD5tP1NnqeA1g
3rd rowIhHSD5tP1NnqeA1g
4th row7Zr54t-YOVCMSUWw
5th row7Zr54t-YOVCMSUWw

Common Values

ValueCountFrequency (%)
7T/yP+n/OW8uEJB3 330
14.0%
VHciCTdcN8uLLIy0 330
14.0%
WF8pC3HscIBm6Oq- 330
14.0%
Z4WqEac4BZQhQ/eJ 330
14.0%
j1hPDn/zHYJDJtt1 173
7.3%
9SnEimmWcRSJzDB8 173
7.3%
uYe3ySd/TK2+M+iU 173
7.3%
KfINVt7uaBeUTeDN 72
 
3.0%
o1/oEQszPZgSwChB 72
 
3.0%
5LgVxDgzLA5I6DDi 68
 
2.9%
Other values (39) 310
13.1%

Length

2023-06-07T21:52:02.404491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7t/yp+n/ow8uejb3 330
14.0%
z4wqeac4bzqhq/ej 330
14.0%
vhcictdcn8ulliy0 330
14.0%
wf8pc3hscibm6oq 330
14.0%
j1hpdn/zhyjdjtt1 173
7.3%
9sneimmwcrsjzdb8 173
7.3%
uye3ysd/tk2+m+iu 173
7.3%
kfinvt7uabeutedn 72
 
3.0%
o1/oeqszpzgswchb 72
 
3.0%
5lgvxdgzla5i6ddi 68
 
2.9%
Other values (39) 310
13.1%

Most occurring characters

ValueCountFrequency (%)
c 1569
 
4.2%
/ 1491
 
3.9%
B 1429
 
3.8%
W 1281
 
3.4%
8 1211
 
3.2%
J 1206
 
3.2%
T 1015
 
2.7%
u 991
 
2.6%
E 952
 
2.5%
3 931
 
2.5%
Other values (55) 25700
68.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17286
45.8%
Lowercase Letter 12520
33.1%
Decimal Number 5422
 
14.4%
Other Punctuation 1491
 
3.9%
Math Symbol 724
 
1.9%
Dash Punctuation 333
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 1569
 
12.5%
u 991
 
7.9%
y 852
 
6.8%
i 831
 
6.6%
h 779
 
6.2%
q 770
 
6.2%
n 734
 
5.9%
m 701
 
5.6%
e 679
 
5.4%
d 563
 
4.5%
Other values (16) 4051
32.4%
Uppercase Letter
ValueCountFrequency (%)
B 1429
 
8.3%
W 1281
 
7.4%
J 1206
 
7.0%
T 1015
 
5.9%
E 952
 
5.5%
D 928
 
5.4%
L 906
 
5.2%
H 858
 
5.0%
C 850
 
4.9%
I 821
 
4.7%
Other values (16) 7040
40.7%
Decimal Number
ValueCountFrequency (%)
8 1211
22.3%
3 931
17.2%
4 723
13.3%
7 566
10.4%
6 470
 
8.7%
1 453
 
8.4%
0 372
 
6.9%
2 282
 
5.2%
9 222
 
4.1%
5 192
 
3.5%
Other Punctuation
ValueCountFrequency (%)
/ 1491
100.0%
Math Symbol
ValueCountFrequency (%)
+ 724
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 333
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29806
78.9%
Common 7970
 
21.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 1569
 
5.3%
B 1429
 
4.8%
W 1281
 
4.3%
J 1206
 
4.0%
T 1015
 
3.4%
u 991
 
3.3%
E 952
 
3.2%
D 928
 
3.1%
L 906
 
3.0%
H 858
 
2.9%
Other values (42) 18671
62.6%
Common
ValueCountFrequency (%)
/ 1491
18.7%
8 1211
15.2%
3 931
11.7%
+ 724
9.1%
4 723
9.1%
7 566
 
7.1%
6 470
 
5.9%
1 453
 
5.7%
0 372
 
4.7%
- 333
 
4.2%
Other values (3) 696
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37776
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 1569
 
4.2%
/ 1491
 
3.9%
B 1429
 
3.8%
W 1281
 
3.4%
8 1211
 
3.2%
J 1206
 
3.2%
T 1015
 
2.7%
u 991
 
2.6%
E 952
 
2.5%
3 931
 
2.5%
Other values (55) 25700
68.0%

Interactions

2023-06-07T21:51:49.496728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:40.240884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:41.472253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:42.554476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:44.098304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:45.168965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:46.231923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:47.329841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:48.385868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:49.659775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:40.404828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:41.608083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:42.683354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:44.244239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:45.293791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:46.356969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:47.450016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:48.511922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:49.781578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:40.545838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:41.735636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:42.798901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:44.357941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:45.408400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:46.475456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:47.561646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:48.627889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:49.897102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:40.685787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:41.856542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:42.920830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:44.477308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:45.529154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:46.601182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:47.681698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:48.756611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:50.008589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:40.813735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:41.973006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:43.042062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:44.589778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:45.644555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:46.724905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:47.800789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:48.874557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:50.126254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:40.939456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:42.092470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:43.160769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:44.703734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:45.759987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:46.844597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:47.915560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:48.993447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:50.243634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:41.075899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:42.210829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:43.285477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:44.822690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:45.881187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:46.970124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:48.040138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:49.124783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:50.353567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:41.203896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:42.321994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:43.400297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:44.935290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:45.993490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:47.090536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:48.152712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:49.241163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:50.470492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:41.348855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:42.440720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:43.982090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:45.061302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:46.120164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:47.215229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:48.270833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-07T21:51:49.365821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-06-07T21:52:02.536027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
fullVisitorIdonPageTimenextTimeuser_active_daystotal_visit_countstotal_unique_sessiontotal_time_spenttime_spent_per_sessiontimeOnPageAudienceIDLotameIDyNamebrowseroperatingSystemoperatingSystemVersionisMobilemobileDeviceBrandingmobileDeviceModelmobileDeviceInfomobileDeviceMarketingNamescreenResolutionlanguagesubContinentcountryregioncitysub_secGAClientIdcookie_id
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time_spent_per_session0.0550.5480.697-0.0270.146-0.0240.2581.0000.1720.3140.2740.3140.3140.3220.3030.1170.3030.1550.2160.2250.2180.2670.1280.3640.3700.2120.3370.1600.2740.268
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operatingSystem0.5660.1770.1810.7530.7500.6660.3400.3030.0000.5850.9910.5850.5850.0691.0000.5590.9961.0001.0000.9931.0000.9960.2200.4260.4370.5550.8380.2830.9910.990
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isMobile0.5660.1770.1810.7530.7500.6660.3400.3030.0000.5850.9910.5850.5850.0690.9960.5591.0001.0001.0000.9931.0000.9960.2200.4260.4370.5550.8380.2830.9910.990
mobileDeviceBranding0.4640.0530.0800.6520.5510.5550.5390.1550.1150.6430.9940.6430.6430.5981.0000.6231.0001.0000.9950.9950.8070.7210.5201.0000.8350.5560.5620.4460.9940.993
mobileDeviceModel0.9840.0000.1110.9310.9320.9470.9650.2160.1630.9841.0000.9840.9840.9841.0000.9951.0000.9951.0001.0000.9990.9960.8081.0000.9710.9530.8480.3441.0000.998
mobileDeviceInfo0.9360.0460.1210.9000.9100.9200.8890.2250.1600.9760.9980.9760.9760.9840.9930.8900.9930.9951.0001.0000.9990.8960.8100.4190.7480.7320.6880.3250.9980.997
mobileDeviceMarketingName0.8940.0410.1160.8710.9000.9080.9660.2180.1610.9610.9980.9610.9610.9851.0000.7911.0000.8070.9990.9991.0000.9170.7201.0000.9720.9490.8440.3440.9980.997
screenResolution0.8500.1140.1530.8420.9030.8730.9010.2670.1410.9450.9960.9450.9450.9320.9960.6210.9960.7210.9960.8960.9171.0000.5390.9770.9330.7200.6100.2840.9960.994
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subContinent0.5030.0950.0330.5790.4010.5680.5850.3640.0290.2790.9730.2790.2790.0140.4260.2280.4261.0001.0000.4191.0000.9770.0861.0001.0000.9980.9780.4420.9730.972
country0.6220.0980.1750.8160.7610.4930.4920.3700.2410.5150.9830.5150.5150.6530.4370.4310.4370.8350.9710.7480.9720.9330.2101.0001.0000.9980.9870.3620.9830.981
region0.6400.0730.0990.6160.6440.5640.7700.2120.1170.8530.9090.8530.8530.6930.5550.4760.5550.5560.9530.7320.9490.7200.3940.9980.9981.0000.9960.2880.9090.907
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sub_sec0.3140.1420.1240.3960.3820.4510.4420.1600.1900.3000.3130.3000.3000.1990.2830.2110.2830.4460.3440.3250.3440.2840.2410.4420.3620.2880.2881.0000.3130.273
GAClientId0.9930.0880.1420.9650.9770.9770.9720.2740.1550.9921.0000.9920.9920.9920.9910.9930.9910.9941.0000.9980.9980.9960.8280.9730.9830.9090.8400.3131.0000.998
cookie_id0.9910.0690.1320.9630.9750.9750.9710.2680.1450.9900.9980.9900.9900.9900.9900.9920.9900.9930.9980.9970.9970.9940.8260.9720.9810.9070.8380.2730.9981.000

Missing values

2023-06-07T21:51:50.708984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-07T21:51:51.571972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-07T21:51:51.958091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

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605818744cbb561a1af5a40eb684d55a8c0b2047a0RMG - 1PD - Declared - HHI - <RM1,500\tHHI10531342589549778032ChromeAndroid8.1.0TrueXiaomiRedmi 6AtouchscreenXiaomi Redmi 6A(not set)360x720en-usAsiaSoutheast AsiaMalaysiaFederal Territory of Kuala LumpurKuala Lumpur/suami-jual-isteri-peroleh-hampir-rm3k-sebulan-saya-sakit-hati-tengok-dia-lakukan-hubungan-intim-tapiNaNSuami Jual Isteri, Peroleh Hampir RM3k Sebulan – 'Saya Sakit Hati Tengok Dia Lakukan Hubungan Intim Tapi..'https://ohbulan.com/suami-jual-isteri-peroleh-hampir-rm3k-sebulan-saya-sakit-hati-tengok-dia-lakukan-hubungan-intim-tapi1030151437735435735.0154.040758amp-35vZcqfyySaxus6_748cgwIhHSD5tP1NnqeA1g
1844809023e74bb87a1abb30abf105f2900a65e8425000RMG - 1PD - Declared - HHI - RM1.5K - RM5K\tHHI1158781765718776901ChromeWindows7FalseNaNNaNNaN(not set)NaN1600x900en-usAsiaSoutheast AsiaMalaysiaSelangorBatu Caves/rakyat-malaysia-maut-dibunuh-di-australia-didakwa-jadi-penjaga-1000-pokok-ganja-bagi-kumpulan-jenayahNaNRakyat Malaysia Maut 'Dibunuh' Di Australia, Didakwa Jadi Penjaga 1,000 Pokok Ganja Bagi Kumpulan Jenayahhttps://ohbulan.com/rakyat-malaysia-maut-dibunuh-di-australia-didakwa-jadi-penjaga-1000-pokok-ganja-bagi-kumpulan-jenayah0126729523781385.0217.0126729269799904.15748373177Zr54t-YOVCMSUWw
1845809023e74bb87a1abb30abf105f2900a65e8425000RMG - 1PD - Declared - HHI - RM1.5K - RM5K\tHHI1158781765718776901ChromeWindows7FalseNaNNaNNaN(not set)NaN1600x900en-usAsiaSoutheast AsiaMalaysiaSelangorBatu Caves/datuk-74-tahun-maut-selepas-tunai-hajat-di-rumah-pelacuran-polis-jumpa-ubat-kuatNaNDatuk 74 Tahun Maut Selepas Tunai 'Hajat' Di Rumah Pelacuran, Polis Jumpa Ubat Kuathttps://ohbulan.com/datuk-74-tahun-maut-selepas-tunai-hajat-di-rumah-pelacuran-polis-jumpa-ubat-kuat126729207157523781385.0217.080428269799904.15748373177Zr54t-YOVCMSUWw
1846809023e74bb87a1abb30abf105f2900a65e8425000RMG - 1PD - Declared - HHI - RM1.5K - RM5K\tHHI1158781765718776901ChromeWindows7FalseNaNNaNNaN(not set)NaN1600x900en-usAsiaSoutheast AsiaMalaysiaSelangorBatu Caves/berlaku-pendarahan-tisu-koyak-keputusan-bedah-siasat-kali-kedua-sahkan-luka-paha-kanan-tangmo-terjadi-sebelum-kematianNaNBerlaku Pendarahan & Tisu Koyak, Keputusan Awal Bedah Siasat Kedua Sahkan Luka Paha Kanan Tangmo Terjadi Sebelum Kematianhttps://ohbulan.com/berlaku-pendarahan-tisu-koyak-keputusan-bedah-siasat-kali-kedua-sahkan-luka-paha-kanan-tangmo-terjadi-sebelum-kematian44441142871523781385.0203.098430269799904.15748373177Zr54t-YOVCMSUWw
407981874478667affe553713e0aed6c72e0eadc9f0RMG - 1PD - Declared - HHI - <RM1,500\tHHI1316008586946977000ChromeAndroid9TrueSamsungSM-J730GtouchscreenSamsung SM-J730G Galaxy J7 (2017)Galaxy J7 (2017)412x732en-gbAsiaSoutheast AsiaMalaysiaFederal Territory of Kuala LumpurKuala Lumpur/news//nation/Rising costs push up buffet priceshttps://www.nst.com.my/news/nation/2022/04/787223/rising-costs-push-buffet-prices3368169647741381970.0170.0166279306407126.1515625704gya7xSV0gWTO5EBb
408081874478667affe553713e0aed6c72e0eadc9f0RMG - 1PD - Declared - HHI - <RM1,500\tHHI1316008586946977000ChromeAndroid9TrueSamsungSM-J730GtouchscreenSamsung SM-J730G Galaxy J7 (2017)Galaxy J7 (2017)412x732en-gbAsiaSoutheast AsiaMalaysiaFederal Territory of Kuala LumpurKuala Lumpur/news//nation/Speculations over Tengku Zafrul's 'resignation' answeredhttps://www.nst.com.my/news/nation/2022/03/782746/speculations-over-tengku-zafruls-resignation-answered4053671784741381970.0672.0667731306407126.1515625704gya7xSV0gWTO5EBb
4945809024debc5687c4f166fba9ae0f460f4484a10000RMG - 1PD - Declared - HHI - RM5k - 10k+\tHHI1345556031535805740ChromeAndroid9TrueAsusX00TDtouchscreenAsus X00TD Zenfone Max Pro (M1) ZB601KLZenfone Max Pro (M1) ZB601KL360x720en-gbAsiaSoutheast AsiaMalaysiaFederal Territory of Kuala LumpurKuala Lumpur/video-bekas-juara-hero-remaja-teruskan-karier-demi-impian-ibu-tercintaNaN[VIDEO] Bekas Juara Hero Remaja Teruskan Karier Demi Impian Ibu Tercintahttps://ohbulan.com/video-bekas-juara-hero-remaja-teruskan-karier-demi-impian-ibu-tercinta029241101123121768.034.029241amp-jVimM0G9J9SlML2eQ53vcC_UUDM3RO4vtxYz2L7jrewuTtmZh1DNqghBK6aA4ravvznB7rLMv2lasMz5
4946809024debc5687c4f166fba9ae0f460f4484a10000RMG - 1PD - Declared - HHI - RM5k - 10k+\tHHI1345556031535805740ChromeAndroid9TrueAsusX00TDtouchscreenAsus X00TD Zenfone Max Pro (M1) ZB601KLZenfone Max Pro (M1) ZB601KL360x720en-gbAsiaSoutheast AsiaMalaysiaSelangorAmpang Jaya/video-aedy-ashraf-buat-kejutan-lamar-amelia-henderson-depan-peminat-2-tahun-saya-tunggu-awakNaN[VIDEO] Aedy Ashraf Buat Kejutan, Lamar Amelia Henderson Depan Peminat – '2 Tahun Saya Tunggu Awak'https://ohbulan.com/video-aedy-ashraf-buat-kejutan-lamar-amelia-henderson-depan-peminat-2-tahun-saya-tunggu-awak074156101123121768.0131.074156amp-jVimM0G9J9SlML2eQ53vcC_UUDM3RO4vtxYz2L7jrewuTtmZh1DNqghBK6aA4ravvznB7rLMv2lasMz5
AudienceIDLotameIDyNameTypefullVisitorIdbrowseroperatingSystemoperatingSystemVersionisMobilemobileDeviceBrandingmobileDeviceModelmobileInputSelectormobileDeviceInfomobileDeviceMarketingNamescreenResolutionlanguagecontinentsubContinentcountryregioncitysecsub_secpageTitleurlonPageTimenextTimeuser_active_daystotal_visit_countstotal_unique_sessiontotal_time_spenttime_spent_per_sessiontimeOnPageGAClientIdcookie_id
88331818744f301629668799c18317f0efd9ae7da610RMG - 1PD - Declared - HHI - <RM1,500\tHHI9121685818671703657ChromeAndroid9TrueRazerPhone 2touchscreenRazer Phone 2(not set)480x854msAsiaSoutheast AsiaMalaysiaFederal Territory of Kuala LumpurKuala Lumpur/doktor-masih-tak-dapat-penyelesaian-amyra-rosli-jatuh-sakit-sampai-kaki-bengkak-amar-baharin-minta-orang-ramai-kongsi-cara-rawatanNaN'Doktor Masih Tak Dapat Penyelesaian' – Amyra Rosli Jatuh Sakit Sampai Kaki Bengkak, Amar Baharin Minta Orang Ramai Kongsi Cara Rawatanhttps://ohbulan.com/doktor-masih-tak-dapat-penyelesaian-amyra-rosli-jatuh-sakit-sampai-kaki-bengkak-amar-baharin-minta-orang-ramai-kongsi-cara-rawatan079507112261611206.0148.0795072123807980.1587881577lrD3OVThuKF+rJiL
88332818744f301629668799c18317f0efd9ae7da610RMG - 1PD - Declared - HHI - <RM1,500\tHHI9121685818671703657ChromeAndroid9TrueRazerPhone 2touchscreenRazer Phone 2(not set)480x854msAsiaSoutheast AsiaMalaysiaFederal Territory of Kuala LumpurKuala Lumpur/kantoi-masuk-bilik-tidur-anak-tiri-isteri-tetak-kemaluan-suami-lepas-tengok-rakaman-web-camNaNKantoi Masuk Bilik Tidur Anak Tiri, Isteri Tetak Kemaluan Suami Lepas Tengok Rakaman 'Web Cam'https://ohbulan.com/kantoi-masuk-bilik-tidur-anak-tiri-isteri-tetak-kemaluan-suami-lepas-tengok-rakaman-web-cam7950789962112261611206.0148.0104552123807980.1587881577lrD3OVThuKF+rJiL
88333818744f301629668799c18317f0efd9ae7da610RMG - 1PD - Declared - HHI - <RM1,500\tHHI9121685818671703657ChromeAndroid9TrueRazerPhone 2touchscreenRazer Phone 2(not set)480x854msAsiaSoutheast AsiaMalaysiaFederal Territory of Kuala LumpurKuala Lumpur/kantoi-masuk-bilik-tidur-anak-tiri-isteri-tetak-kemaluan-suami-lepas-tengok-rakaman-web-camNaNKantoi Masuk Bilik Tidur Anak Tiri, Isteri Tetak Kemaluan Suami Lepas Tengok Rakaman 'Web Cam'https://ohbulan.com/kantoi-masuk-bilik-tidur-anak-tiri-isteri-tetak-kemaluan-suami-lepas-tengok-rakaman-web-cam89962148330112261611206.0148.0583682123807980.1587881577lrD3OVThuKF+rJiL
8989380902490d7f925b2f2bc418ab77befee9a410610000RMG - 1PD - Declared - HHI - RM5k - 10k+\tHHI97055928790379460ChromeAndroid10TrueHuaweiEML-L29touchscreenHuawei EML-L29 P20P20320x665en-gbAsiaSoutheast AsiaMalaysiaFederal Territory of Kuala LumpurKuala Lumpur/news//nation/Covid-19: Health Ministry reports more recoveries than new infectionshttps://www.nst.com.my/news/nation/2022/03/784460/covid-19-health-ministry-reports-more-recoveries-new-infections318348332215313199295.0483.048013922597594.159209363613iqyWv/9MLXoQl5
8989480902490d7f925b2f2bc418ab77befee9a410610000RMG - 1PD - Declared - HHI - RM5k - 10k+\tHHI97055928790379460ChromeAndroid10TrueHuaweiEML-L29touchscreenHuawei EML-L29 P20P20320x665en-gbAsiaSoutheast AsiaMalaysiaFederal Territory of Kuala LumpurKuala Lumpur/business//2022/Surging semiconductor ASPs, higher oil prices to boost DNeX's earningshttps://www.nst.com.my/business/2022/03/782976/surging-semiconductor-asps-higher-oil-prices-boost-dnexs-earnings2585182045815313199295.01985.0181787322597594.159209363613iqyWv/9MLXoQl5
8989580902490d7f925b2f2bc418ab77befee9a410610000RMG - 1PD - Declared - HHI - RM5k - 10k+\tHHI97055928790379460ChromeAndroid10TrueHuaweiEML-L29touchscreenHuawei EML-L29 P20P20320x665en-gbAsiaSoutheast AsiaMalaysiaFederal Territory of Kuala LumpurKuala Lumpur/business//2022/'Let market forces decide Sapura Energy's fate'https://www.nst.com.my/business/2022/03/782967/let-market-forces-decide-sapura-energys-fate1820458198473815313199295.01985.016428022597594.159209363613iqyWv/9MLXoQl5
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8989780902490d7f925b2f2bc418ab77befee9a410610000RMG - 1PD - Declared - HHI - RM5k - 10k+\tHHI97055928790379460ChromeAndroid10TrueHuaweiEML-L29touchscreenHuawei EML-L29 P20P20320x665en-gbAsiaSoutheast AsiaMalaysiaFederal Territory of Kuala LumpurKuala Lumpur/business//2022/Surging semiconductor ASPs, higher oil prices to boost DNeX's earningshttps://www.nst.com.my/business/2022/03/782976/surging-semiconductor-asps-higher-oil-prices-boost-dnexs-earnings28413693715313199295.0244.03409622597594.159209363613iqyWv/9MLXoQl5
8989880902490d7f925b2f2bc418ab77befee9a410610000RMG - 1PD - Declared - HHI - RM5k - 10k+\tHHI97055928790379460ChromeAndroid10TrueHuaweiEML-L29touchscreenHuawei EML-L29 P20P20320x665en-gbAsiaSoutheast AsiaMalaysiaFederal Territory of Kuala LumpurKuala Lumpur/business//2022/Surging semiconductor ASPs, higher oil prices to boost DNeX's earningshttps://www.nst.com.my/business/2022/03/782976/surging-semiconductor-asps-higher-oil-prices-boost-dnexs-earnings369378442215313199295.0244.04748522597594.159209363613iqyWv/9MLXoQl5
8989980902490d7f925b2f2bc418ab77befee9a410610000RMG - 1PD - Declared - HHI - RM5k - 10k+\tHHI97055928790379460ChromeAndroid10TrueHuaweiEML-L29touchscreenHuawei EML-L29 P20P20320x665en-gbAsiaSoutheast AsiaMalaysiaFederal Territory of Kuala LumpurKuala Lumpur/business//2022/Surging semiconductor ASPs, higher oil prices to boost DNeX's earningshttps://www.nst.com.my/business/2022/03/782976/surging-semiconductor-asps-higher-oil-prices-boost-dnexs-earnings8442224413715313199295.0244.015971522597594.159209363613iqyWv/9MLXoQl5